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# Meta and Multi-Task Learning for Action Recognition:
A Survey

Gavriela Senteri, Sotiris Manitsaris, Alina Glushkova

► To cite this version:

Gavriela Senteri, Sotiris Manitsaris, Alina Glushkova. Meta and Multi-Task Learning for Action Recognition: A Survey. IEEE Access, 2026, 10.1109/access.2026.3665252. hal-05516783

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Received 7 December 2025, accepted 4 February 2026. Date of publication 00 xxxx 0000, date of current version 00 xxxx 0000.

Digital Object Identifier 10.1109/ACCESS.2026.3665252

# Meta and Multi-Task Learning for Action Recognition: A Survey

GAVRIELA SENTERI<sup>©</sup>, SOTIRIS MANITSARIS<sup>©</sup>, AND ALINA GLUSHKOVA

Centre for Robotics, Mines Paris-PSL University, 75006 Paris, France

Corresponding author: Gavriela Senteri (gavriela.senteri@minesparis.psl.eu)

This work was supported in part by the Craeft project that has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101094349.; and in part by the Resource Project funded by Program, "Skills and Jobs of the Future" France 2030 managed by the Caisses des Dépôts et Consignations.

**ABSTRACT** Human movement recognition currently plays an important role in fields where human-machine interaction is included, such as virtual reality and rehabilitation. In these cases, the accurate recognition of human actions has become essential, and is currently making significant steps due to the wide use of Machine Learning algorithms. However, the standard methods in that category currently face several challenges that concern their ability to generalize and adapt to new, unseen data, as well as the need for large amounts of data for training. This survey examines how the methods of Multi-Task Learning and Meta Learning have been deployed as strategies to tackle the above challenges. It studies how the two approaches are applied to human action recognition, focusing on the utilized methodologies, data modalities, and architectural approaches, highlighting the way that the first method promotes task-related knowledge sharing, while the second enables adaptation to scarce data. Through the mapping of studies from 2014 to 2025, this survey identifies trends, methodological gaps, and future insights for the development of more adaptive and context-aware recognition systems.

**INDEX TERMS** Action classification, gesture recognition, meta-learning, multi-task learning.

## I. INTRODUCTION

With the recent advances in Machine Learning (ML), human movement recognition has gained increased interest across various fields, such as human-robot interaction, vocational training, and rehabilitation. Movement recognition is central to such cases, for facilitating adaptive systems to better accommodate human operators. Human movement recognition has, however, challenges not only in applying ML approaches and producing robust results, but also affects the dynamics of human movement. The ML challenges concern the data quality and availability, the explainability and interpretability of ML modules, as well as the need for a vast amount of data for training, which involves large computational costs. However, apart from those challenges, the complex nature of human movement adds further challenges to be handled. These challenges can be explained

as a set of levels (Figure 1), with human motion units being the starting point, as the fundamental movement patterns that compose any action or gesture. These patterns include joint rotations, localized trajectories, RGB, or skeleton data. While seemingly simple, the modeling of this layer is not trivial, as it requires accurate capturing, processing, treatment, and representation. Most gesture recognition models start from that point, through an attempt to learn motion cues from discrete frames or timeseries. Right after these motion units, there is the intra-person variability, thus the motion differences within the same person, occurring under varying conditions of fatigue, pain, or injury. The next complexity step concerns the inter-person variability, where the variable anthropometric characteristics and motor range among different people can lead to different executions of the same movement. Following, the hierarchical and contextual complexity shows that human movement can reflect structures, have a context, a specific goal, as well as different meanings, within different environments. All the above levels are characterized by their

The associate editor coordinating the review of this manuscript and approving it for publication was Pavlos I. Lazaridis<sup>©</sup>.
temporal and spatial dynamics. Human actions unfold in space and over time, varying in speed, rhythm, duration, and amplitude, thus, the recognition of human movement requires the understanding of not only what a movement is, but also the way that it evolves. Managing those types of complexity requires the design of effective feature extraction techniques that do not take into consideration redundant or irrelevant information [1], [2], [3], [4], [5].

The above scientific challenges come along with the increasing need of the action recognition community for solutions that can accommodate the intricacies of human movements, leading to advances in ML beyond what has been traditionally done. This community is interested in building technologies capable of precisely interpreting fine details of human movement. Such applications call for high precision, low-latency gesture recognition, robustness, and flexibility towards any user or context without full retraining, meaning that some of the standard ML models cannot be used beyond certain limits. Methods that appear promising in the literature for handling the precedent challenges are Multi-Task Learning (MultiTL) and Meta Learning (MetaL). Both methods offer partial but complementary responses to the aforementioned five-level complexity of human movement. MultiTL leverages knowledge across tasks but struggles with adaptation to new, unseen data, while MetaL emphasizes adaptation but often abstracts away from the structural richness of real-world gestures. Neither method has fully addressed the interaction between spatiotemporal variability, multimodal data, and semantic intent. This survey takes these observations as its starting point, aiming to explore how MultiTL and MetaL are currently applied in human action recognition, what assumptions are made about task structure and movement variability, as well as where future work must be directed, to bridge the gap between the algorithmic progress and the human movement complexity.

Although several surveys on MultiTL and MetaL exist in the literature, to the knowledge of the authors, there is no such survey that focuses on action recognition. This field can particularly benefit from these approaches, due to its characteristics mentioned above. Human movement recognition is characterized by complex temporal and spatial dynamics, multimodal inputs, as well as strong inter- and intra-person variabilities. Such properties introduce challenges that existing surveys do not address and also make human movement recognition a distinct research domain, highlighting the need for a separate study of the respective methods that can be deployed for adaptation and generalization to new, unseen data. Thus, this survey fills that gap by systematically analyzing MultiTL and MetaL, specifically for human movement recognition. More specifically, this work aims to explore how MultiTL is utilized for the exchange of knowledge among action recognition-related tasks, and how MetaL is approached for the adaptation to new, unseen actions, activities, and gestures. A survey focused on these methods will not only highlight their specific advantages

for action recognition but also explore gaps in the existing datasets and methods, proposing future insights in the field.

## A. PAPER ORGANIZATION

This survey aims to provide a review of MultiTL and MetaL, focusing on their employment for human movement recognition. To guide this analysis, the following questions are raised:

* Which learning strategies are most commonly employed in MultiTL and MetaL for movement recognition, and how are they applied across different domains?

* What are the main datasets deployed for gesture recognition in MultiTL and MetaL, and what trends can be observed over the years?

* How is human movement treated, defined, or structured during training and inference in a MultiTL or MetaL setup?

* How do current MultiTL and MetaL approaches address the spatiotemporal complexity of human movement, rather than treating it as a static pattern recognition or computer vision task?

* Are there tendencies in the field of MultiTL and MetaL for action recognition that prioritize visual modalities over embodied data representations? What does this imply about the understanding of action recognition as a research problem?

Following the Introduction, this work continues with a summary of the fundamentals of the two ML approaches (Section II and Section III), and an overview of the existing surveys on MultiTL and MetaL (Section IV), setting the background for this survey. Next, we present sections that focus on MultiTL and MetaL, specifically on human movement recognition (Sections V and VI), analyzing existing approaches and grouping the results into publicly used datasets. This work ends with the discussion (Section VII), future insights, and further conclusions (Section VIII).

## II. INTRODUCTION TO MULTI-TASK LEARNING

In the standard ML approaches, there is always a source task and a target task, used along with their own, separate models. In real-life scenarios, though, dependencies appear, and different tasks can have common characteristics and a common structure. MultiTL is a subfield of ML, where a single model is trained to perform multiple tasks simultaneously, through shared network layers [6]. This way, information is exchanged among them, and knowledge is leveraged to improve their performance, thus MultiTL often provides better performance than Single-Task Learning (STL). Such an approach presents benefits including improved data efficiency, reduced overfitting via shared representations, and faster learning by leveraging auxiliary data [7]. The notion of the “task” in MultiTL is handled by the scientific community in three different ways. Tasks can either denote 1) the classes of a dataset, thus each human action included in an activity, 2) different modalities or types of signal (sound, image,
<figure><img src="image_3.png" /><figcaption>FIGURE 1. The five levels of human movement complexity, ranging from low-level motion units to contextual complexity. These levels add an extra layer to the existing challenges of human movement and further motivates the need for learning approaches able to model the spatiotemporal dynamics and the inter- and intra-person variability of movement.</figcaption></figure>

RGB, depth, optical flow), or 3) different processes (i.e., gesture recognition and image segmentation). The objective of MultiTL is directly related to the computation of the loss L, derived as a combination of the loss of each one of the tasks in the MultiTL scenario, in some cases, weighted by the coefficients or weights, defined according to the saliency of each task. Focusing on human movement, and considering that by default one of the MultiTL tasks will concern human movement recognition, the other tasks implemented in parallel can focus either on movement recognition or analysis, or even seemingly different tasks, which can contribute to improving the recognition performance.

As such, the goal of MultiTL is the minimization of:

$$L = loss_{gr} * w_{gr} + loss_{T_i} * w_{T_i} \quad (1)$$

where lossgr is the loss of the task that concerns gesture recognition, lossT_i, with i ∈ N being the loss of the rest of the MultiTL tasks T and wgr, wT_i the weights for the tasks of gesture recognition, and the rest of the tasks T, respectively. An important challenge in MultiTL concerns the relatedness of the deployed tasks. While task weighting mechanisms and different loss approaches can alleviate the problem, when tasks are poorly related, even these kinds of approaches may result in negative performance. Specifically in gesture recognition, where tasks can involve various levels of complexity, the notion of task-relatedness becomes particularly challenging. The goal is to further explore how task-relatedness is handled in human movement recognition,

as well as whether it still appears as a challenge, or not. It is important to emphasize the fact that MultiTL does not simply concern a parallel training of different tasks through a common network, but an exchange of knowledge among them, through shared model layers.

### III. INTRODUCTION TO META LEARNING

The prefix “meta” is derived from the Greek word “μετα” (meta), which can be translated as “after” or “with”. In a variety of contexts, the prefix “meta” denotes a higher level of abstraction or something self-referential or self-reflective. MetaL allows a model to learn how to learn by benefiting from prior knowledge it has been trained with, to learn a new task, rather than accumulate subject knowledge, and it appears useful for resolving few-shot learning problems [8]. More specifically, having a set of datasets D_i with loss functions L_i, the objective is to find an initialization or set of parameters θ* that allows the model to learn and efficiently generalize when new, unseen data D_target_i appear. Thus, the goal is to optimize θ* and minimize the loss. As such,

$$\theta^* = \operatorname*{argminL}(D_{\text{target}_i}; \theta, \omega^*) \quad (2)$$

with the network now benefiting from the meta-knowledge ω* that acquired during the training phase. In summary, the goal of MultiTL is to improve the performance of multiple related tasks in parallel by leveraging useful information among them, while MetaL aims to find parameters that can enable the respective model to learn new, unseen tasks
effectively. According to the two definitions, MetaL could
be combined with MultiTL to produce models that exchange
knowledge among seen tasks, which can adapt to unseen
ones.

IV. BACKGROUND

A. LITERATURE REVIEWS ON MULTI-TASK LEARNING

The main advantage of MultiTL is connected to its ability
to exchange knowledge among tasks and leverage task
similarities to improve their learning performance. Recent
surveys on the subject have tackled a variety of perspectives,
including how tasks relate to each other, how learning is
optimized across them, as well as how architectures are
designed to support these mechanisms (Figure 2). As such,
the existing surveys on MultiTL are synthesized around three
key topics, task-relatedness, optimization approaches, and
architectural methodologies.

1) TASK-RELATEDNESS

Various surveys [9], [10], [11] highlight that identifying and
modeling task relationships is critical for effective MultiTL
setups. The discussion of negative transfer, where unrelated
tasks harm performance, is present in most of them but is
particularly emphasized in domain-specific studies, such as
reinforcement learning [12] and recommender systems [13].
Interestingly, all of the surveys analyze the importance of
task-relatedness and the fact that there is no specific metric
for verifying the relatedness of tasks in a MultiTL setup, and
empirically selecting tasks is the most mainstream [14], [15],
as most systems still rely on trial and error when deciding
which tasks to combine. Many works however, argue that
task-relatedness can be computed through task similarity,
task correlation, task covariance [16], or even through the
magnitude of the task losses [15]. This observation suggests a
need for solid, automated approaches to dynamically evaluate
task interdependence, or an exploration of such a kind of
metrics. After all, as mentioned in [14], “task similarity is not
binary, but resides in a spectrum”, which needs to be defined
in a way, maybe according to the respective use-case. [16]
acknowledges that Multi-Gaussian Processes and Multi-Task
Relationship Learning attempt to learn task relationships;
however, they are not universally applicable.

An important aspect of task-relatedness is the notion and
the goal of the tasks in MultiTL. Crawshaw [17] comes with
a very structured review, stating that

"As a convention, we interpret MTL to only contain learning settings in which a fixed set of tasks is learned simultaneously, and each task is treated equally. This means that we don't consider training settings that have only a single "main task" with one or more auxiliary tasks, as well as settings in which the set of tasks to learn changes over time. We may, however, discuss models which were designed for such settings, if the ideas from the model are easily applicable to MTL."

while in [10] it is mentioned that the real world is multimodal
and people have to always adapt to new tasks, thus we also
need to treat all tasks equally, so that there is no dominant
gradient among any of them. However, Caruana [6] in the
proposal of MultiTL mentioned that the goal of the method
is not specifically to optimize all tasks at the same time, but
rather to perform knowledge exchange among tasks, with
the objective to improve at least one of them. This state-
ment is supported by [15], which discusses about findings
where optimal MultiTL performance is achieved when the
auxiliary dataset is much smaller than the target dataset,
ideally with a size ratio between 0.01 and 0.1, as larger
auxiliary datasets can degrade performance on the target
task.

2) OPTIMIZATION APPROACHES

Another commonly discussed subject concerns the optimiza-
tion approaches aiming to balance task learning. Different
surveys provide in-depth discussions on loss balancing
techniques [10], [17], including Gradient normalization
methods (GradNorm, PCGrad), uncertainty-weighted loss
balancing, and dynamic task weighting strategies. While the
above approaches have proved to be able to ensure balanced
learning, they are mostly evaluated in computer vision and
NLP contexts. Human action recognition and other domains
with similar complexity may require different balancing
mechanisms due to temporal dependencies or contextual
structure.

3) METHODOLOGY AND ARCHITECTURE

Certain surveys, such as [10] and [17], provide empirical
comparisons of MultiTL architectures, while others, such
as [9] and [16], appear to be more theoretical. Apart from this,
surveys focusing on applied fields (smart transportation and
NLP) attempt to analyze real-world case studies; however,
they do not group findings across different domains. This
highlights a disconnection between theoretical advancements
in MultiTL and their practical implementation. A newer
aspect explored in recent surveys [18] is MultiTL within
the context of foundation models, such as Transformer-
based architectures. In that case, while traditional MultiTL
approaches are usually designed for a fixed set of tasks, the
employment of foundation models, led MultiTL to a new
phase of challenges in the adaptation to multiple, different
tasks at once. In terms of the deployed methodologies,
different ways to categorize MultiTL strategies appear. The
most common method distinction is between hard and soft
parameter sharing, presented by Ruder [14] and further
analyzed in [17]. While these categories remain useful,
more recent surveys argue they are insufficient for capturing
the full design space of MultiTL models, and thus the
nature of the data. References [11] and [18] proposed
to extend this framework by incorporating optimization-
based categorization, discussing how task interactions can
be mediated through multi-objective optimization (e.g.,
<figure><img src="image_4.png" /><figcaption>**FIGURE 2.** A visual timeline of the existing surveys on Multi-Task Learning. The evolution of the field from architecture-centered taxonomies toward foundation-model approaches validates the need for a survey on Multi-Task Learning, specifically for human movement recognition.</figcaption></figure>

MGDA), gradient correction techniques (e.g., PCGrad), and adaptive task weighting (e.g., GradNorm, DWA). In addition to architectural and optimization-based views, surveys have also proposed domain-specific taxonomies. For instance, [17] groups MultiTL models into single-domain, multimodal, learned architecture, and conditional architecture categories, noting that learned and conditional approaches (such as Sluice networks or Mixture-of-Experts) aim to adaptively route shared representations based on task signals. Meanwhile, surveys focused on applied domains, such as [13] in recommender systems, introduce classification schemes based on task relationship discovery, task structure learning, and data modality handling. Reference [18] proposes a taxonomy that includes regularization-based methods, relationship learning, feature propagation, optimization techniques, and pretraining. This work tries to highlight the way that each of the aforementioned approaches addresses different MultiTL challenges. This new grouping of methods is valuable specifically in domains like human movement recognition, which involve varying input modalities and inter-task dependencies that some architectures may fail to capture. Most MultiTL surveys propose taxonomies that focus on the mechanisms of knowledge sharing between tasks, through shared parameters or feature representations. Despite the coverage of MultiTL across domains, none of the surveyed works focus on its applications, specifically on human movement recognition. Thus, the goal of this survey is to further explore how MultiTL has been deployed for temporally dependent gesture recognition tasks, considering both domain-specific task relationships and the limitations of the existing MultiTL frameworks when applied to human movement data.

## B. LITERATURE REVIEWS ON META LEARNING

MetaL has evolved significantly over the years, both in algorithm selection and meta-features engineering. Specifically in the case of human movement recognition, where task structures are complex and usually multimodal, MetaL provides the potential for adaptation and generalization across those complex, unseen patterns. What initially started as a way of choosing the best training model based on prior experience [19], [20] has evolved into a family of methods to generalize through DL, in few-shot or even zero-shot setups, with minimal data and across various domains. The included surveys focus either on the general concept of MetaL, or its application in specific fields, such as the NLPs [21], recommendation systems [22], and healthcare [23].

Across the surveys over the years (Figure 3), a consistent taxonomy of MetaL methods into model-based, optimization-based, and metric-based approaches is observed [24], [25], [26]. Model-based approaches employ architectures with fast adaptation mechanisms to new, unseen data, such as modulated weights that can quickly adapt to them. On the other side, optimization-based approaches aim to learn initializations that can be efficiently fine-tuned. Lastly, metric-based approaches define similarity-based frameworks that allow predictions to be made, usually through pairwise comparisons. These surveys are connected via the common concern that adaptation can be effective only in the case that the distribution from which the support and query sets are drawn is sufficiently stable. That issue of distribution sensitivity is treated as the main issue in MetaL and more specifically across few-shot learning [8], segmentation [27], and NLP applications [21]. References [28], [29], and [30] also stress the poor robustness and performance of MetaL models
<figure><img src="image_5.png" /><figcaption>FIGURE 3. A visual timeline of the existing surveys on Meta Learning. While Meta-Learning has been widely analyzed in areas such as NLP and healthcare, its application to human movement recognition remains limited.</figcaption></figure>

when applied to out-of-distribution tasks. They mention that current benchmarks, widely employed, fail to simulate the distribution shifts in data, thus proposing the creation and use of domain-general benchmarks that better depict those distribution shifts, along with their integration with domain adaptation approaches. This is particularly relevant in human movement recognition scenarios, due to the movement's nature. Reference [8] considered that the standard categorization of MetaL into model-based, optimization-based, and metric-based does not sufficiently depict the MetaL scene and proposed a taxonomy that includes the categories of meta-representation (“what”), meta-objective (“why”), and meta-optimizer (“how”). Similarly, [22], [31] introduced the categories of application-driven and multimodal frameworks MetaL, respectively, while [32] proposed a taxonomy based on a connection of MetaL with continual and online learning. Another main challenge concerns the need for large amounts of data for training purposes, specifically for few and zero-shot purposes [23], [30], [33], also mentioning that hybrid approaches with auxiliary tasks, unsupervised pertaining, or self-supervised representation learning, hint that hybrid MetaL and MultiTL approaches can handle the data challenge.

## 1) TASK DISTRIBUTION AND THE CONNECTION TO MULTI-TASK LEARNING

The issue is connected to one of the main challenges in MultiTL, that of task-relatedness. This shows a deeper

connection between MetaL and MultiTL, where hybrid versions of the two approaches would provide interesting results. Both paradigms are trying to handle the challenge of generalizing across variations that lie either in their tasks in MetaL or in their task components for MultiTL. The assumption that relatedness exists in some way or another is usually implicit but rarely quantified. References [26], [34], and [35] also observed that MetaL models assume that tasks are drawn from unimodal distributions, which is unrealistic for real-world applications, let alone be applied in a complex human movement recognition scenario. References [22], [23] went beyond that and discussed the fact that diverse tasks require adaptive learning strategies, as a single MetaL strategy may not generalize well.

## C. EXCLUSION CRITERIA AND SEARCH STRATEGY

The goal of this work is to further examine the existing work on both MultiTL and MetaL, specifically applied to human movement. For the identification of the works to be included in this survey within the range of the years 2014 up to 2025, the SCOPUS, IEEE Xplore, and Science Direct digital libraries and the peer-reviewed works included in them were explored, with the same prompt in their advanced search, being:

“(multi-task learning” OR “multi task learning” OR “multitask learning” OR “multi-task deep learning” AND “action recognition” OR “gesture recognition” OR “motion recognition” OR “activity recognition”) for MultiTL, and
<figure><img src="image_6.png" /><figcaption>FIGURE 4. PRISMA flowchart for the works on MultiTL and MetaL for human motion, action, or gesture recognition, illustrating the filtering procedure from initial database search to the final inclusion.</figcaption></figure>

("meta learning" OR "meta-learning" OR "meta" OR "meta deep learning") AND ("action recognition" OR "gesture recognition" OR "motion recognition" OR "activity recognition") for MetaL.

After the initial search, some of the results were removed due to being irrelevant to the subject, while works that concern concepts such as emotion recognition, face or object detection, pose estimation, gaze detection, meta-analysis, meta-heuristics, ensemble learning, incremental learning, and micro-expression recognition were also excluded. The remaining works for both learning approaches were fully assessed for eligibility, with some of them being excluded due to not implementing a true MultiTL or MetaL framework, despite mentioning related keywords in their title, keywords section, or abstract. More specifically, certain articles were found to misuse the terms of MultiTL, or MetaL, to describe transfer learning, or simple fine-tuning setups, which were excluded from the final scanning step. The number of the final works considered in this article is shown in the PRISMA flowchart in Figure 4. This research is not exhaustive and up to the knowledge of the authors, there is no other survey on MultiTL and MetaL that focuses on human motion recognition. Such work aims to perform a cartography of the existing approaches in the field and facilitate new research on human motion recognition through its coherent grouping, the identified challenges, and future pathways.

## V. MULTI-TASK LEARNING FOR HUMAN MOVEMENT RECOGNITION

This section surveys how MultiTL has evolved in the field of human movement recognition, with models simultaneously addressing multiple related objectives to uncover more generalizable representations of human motion (Figure 5). On one hand, there has been a clear trend towards more sophisticated learning schemes, trying to identify when and what to share, but on the other hand, simpler shared-layer designs with fixed task loss weights are still widely used, particularly when tasks are aligned or closely related. Thus, an evolution is observed, but simple MultiTL architectures

remain relevant whenever their inductive bias matches the problem's structure. This section will further examine this evolution and attempt to identify any trends and gaps, focusing on human movement.

### A. FROM STATIC RELATIONS TO THE NEED FOR ADAPTIVE STRUCTURES

Multiple MultiTL approaches appear towards the beginning of the decade that already start to form trends. However, even at that point, the notion of a "task" is already broadened, from different body parts to multiple camera views, to various sensing modalities. Main trends emerged that together map the landscape of early MultiTL for action recognition. Bag-of-Words models such as [36] and its multiview extension [37] connect the linear predictors of limbs through a Laplacian penalty, while a sparsity term lets only the most informative joints dominate the decision. Following the same sense, [38] nests those limb tasks inside whole-body tasks and also couples them across views, capturing both local detail and global pose. A variant, [39], pushes the same principle into time, where each limb is further sliced into short motion cells, producing a block-diagonal Laplacian that fuses spatial and temporal continuity. All four models succeed when the graph is well matched to the data, yet their MultiTL loss is always a weighted sum of error, whose coefficients are cross-validated once and then frozen, leaving them unable to adapt if tasks drift during training. The second direction during these years leans more towards data-focused approaches. Reference [40] decomposes RGB and depth features into common and channel-specific subspaces learned jointly with the classifier, while [41] performs a linear-discriminant analysis so that the similarity graph between camera views is itself a regularized parameter. In the Bayesian side, [42] introduces hierarchical priors that couple max-margin classifiers across modalities and action labels, letting the data define how strongly each one should influence the other. Complementing the above approaches, [43] clusters action labels into "super-categories" and connects the corresponding classifiers through a shared low-rank
projection. Grouping labels by motion mechanics lets shared parameters capture repetitive patterns that occur across nominally different actions. Collectively, these works pave the way to a first exploration of one of the main challenges in MultiTL, task-relatedness, however, they stay within the spectrum of using static, global weights in the MultiTL loss function, thus preventing the tasks from evolving together during the learning process. During this period, there is also a focus on unsupervised setups. Reference [44] treats each user in an egocentric-video collection as a task and aligns activity clusters across users with Earth-Mover distances, while [45] applies a similar multi-task clustering to ambient surveillance. DL, meanwhile was beginning to rise [46], [47].

The above shows that in the period from 2014 to 2016, researchers started connecting the definition of tasks in their respective approaches to their relatedness, which seems like a valid path, and it remains to be verified whether this strategy needs to be further explored. Three main gaps begin to emerge, concerning the relationships among tasks, which are most often assumed or learned once and for all, the MultiTL loss terms, which are summed with static weights, as well as the fact that the layers of human movement complexity have not been handled up to now.

## B. EARLY EVOLUTION IN TASK DEFINITION AND RELATEDNESS

As the field moved into 2017, an evolution from the previous years can be observed in terms of how tasks are defined, within the range of frame-level, clip-level, and modality-level, yet the core structural limitations identified in earlier works persisted. In contrast to earlier strategies that encoded task-relatedness via fixed spatial graphs or hierarchical priors, works in 2017 explored MultiTL through modular task decomposition. One notable example is the joint optimization of action proposal, classification, and temporal localization in untrimmed videos introduced by [48], which replaces the traditional sequential pipeline with a simultaneous three-branch architecture. Here, the tasks correspond to different roles in the detection pipeline, including binary classification, categorical prediction, and temporal regression. Although promising in terms of computational efficiency and performance, this functional decomposition assumes rather than learns the relatedness of the branches, and the loss remains a static sum of three components, each contributing equally across all training epochs.

In earlier works, spatial task-relatedness was explicitly encoded using fixed relational graphs over body parts. These models assumed that each limb or joint was a separate task and tried to couple or decouple their influence. The downside was that this structure was static, and the weightings in the loss function were fixed. Reference [49] takes a different route to a similar problem. Rather than explicitly modeling a spatial graph over the body, it decomposes a skeleton sequence into feature vectors based on different spatial relationships and treats each one as a separate task. However,

this does not preserve the structural expressiveness of the earlier graph-based models. There is no joint selection via sparsity, and no learned or imposed graph among tasks. Instead, it reverts to a uniform task treatment, summing task losses without adaptive weighting and averaging predictions. On the representation side, a similar situation is observed in multi-view and cross-view setups, where views or sensors are again treated as tasks. In [50] and [51], dense trajectory or Bag-of-Visual-Words (BoVW) features from each camera view are fed into regularized MultiTL models that penalize dissimilar solutions across views. These methods succeed in capturing view-level redundancies, yet the weight-sharing mechanisms are again manually imposed and lack responsiveness to the data distribution encountered during training.

While these models focused on input-level variation, others began to explore task definitions at a more latent level, further investigating the structure of human movement itself. Reference [52] framed joint selection as a multi-instance multi-task problem, grouping joints into configurations (instances) inside a bag (action class). This introduced an implicit hierarchy of importance among body parts, which was further refined through group-sparsity constraints across tasks. Yet again, the MultiTL loss was summed up using static coefficients, and the deployed system offered no dynamic means to modulate inter-task influences during training. As a variant, [53] introduced the notion of “skelets”, thus joint configurations optimized via a latent max-margin MultiTL framework. This model explicitly separates common and private components across tasks using structured sparsity, and is among the few 2017 models that attempt to disentangle shared and task-specific representations for action classes. Still, the latent relationships are captured via regularization, not learned dynamically through a task-adaptive mechanism. Finally, a shift towards unsupervised MultiTL is evidenced in [54], which reframes human action recognition as a clustering problem. The proposed Multi-Task Information Bottleneck method leverages shared high-level vocabularies across tasks to align clusters. Although this introduces a more probabilistic and task-aware treatment of relatedness, the approach is limited to the unsupervised setting and does not directly address loss formulation in supervised MultiTL models.

An introduction of a more varied set of task formulations in MultiTL for human action recognition is observed, moving beyond earlier modality-based setups to include tasks defined over body segments, temporal slices, and latent joint groupings. A few models begin to engage with aspects of human movement complexity at the feature level, particularly through mid-level representations or structured regularization. However, these contributions remain mostly exploratory and do not fundamentally alter the core mechanisms of task coordination. In nearly all cases, task relationships are statically imposed, and loss functions continue to rely on fixed aggregation schemes without dynamic adjustment during training. As such, the three central limitations - fixed task relationships, static loss weighting, and limited
engagement with the temporal and spatial structure of movement - remain underexplored.

### C. TOWARD TASK-AWARE OPTIMIZATION THROUGH AUXILIARY GUIDANCE AND CROSS-DOMAIN TRANSFER

After the aforementioned task definitions, the literature of 2018 focuses on “why” multiple tasks should be learned together, rather than “how” many could be packed into one network. This year is characterized by the use of auxiliary tasks that are able to act as learning supervisors, cross-domain knowledge sharing, where sensing modalities no longer overlap, as well as attempts to let a network learn how much each task should influence the other. These three characteristics move towards action recognition optimization approaches, instead of only focusing on architectures.

A first group of works focused on side objectives, with the aim of sharpening action features. Reference [55] pairs action labels with dense optical-flow prediction, encouraging a single RGB branch to encode motion cues that earlier two-stream designs had to import. Reference [56] uses 2D and 3D pose regression as an auxiliary head, pushing the backbone to learn the kinematic structure before classifying an activity. Following the same direction, [57] adds person-identity recognition as an auxiliary task to skeleton-based action classification, exploring how disentangling “who” from “what” can act as a regularizer. These works confirmed that carefully chosen auxiliary tasks can improve the recognition accuracy, yet all three rely on fixed coefficients to balance their losses; thus, the respective network never adjusts how strongly the flow, pose, or identity task affects the rest and leads towards the main objective. A second group of works attempted to face the practical reality of the publicly available datasets, which rarely share common sensor setups or subjects. However, using different datasets in a single MultiTL setup offers complementary supervision [58], as well as cross-domain generalization [59]. At that point, two works appear as the first ones in that decade that attempt task-weighting approaches [60] and [61]. The first one adds a gate network that looks at each input video and decides how much to “mix” an RGB stream with a flow stream. These mixing weights are learned together with the action classifier, so they can differ from one video to the next instead of being fixed for all data. On the other side, [61] couples spatial object detection with temporal localization in a single end-to-end detector, allowing shared early layers to decide how much localization error should inform classification gradients. Both studies mark a shift from static hyperparameters to adaptive influence. Finally, [62] revisits the idea of treating each frame or clip of a skeleton sequence as a separate task and processes them through a Multi-Task CNN. Although its representation is richer than earlier per-joint Laplacians, its optimization remains a sum of losses. Auxiliary tasks were no longer added only for an extra level of supervision, but were chosen for the specific movement cues they could provide. In nearly every

work, task-relatedness, which has appeared to be the main challenge in MultiTL, was still assumed from the beginning, rather than inferred from the data; however, the community starts to acknowledge the need to understand how tasks should communicate, without having developed optimization strategies that can reply to that question.

The year 2018 left open questions on how strongly the auxiliary tasks need to be involved in the learning of the target task, with the works of 2019 further pushing their methodologies in that way. MultiTL is now used as a means of controlling information flow, sometimes as a supporting tool to the main methodology, while others as the main focus. Reference [63] breaks every human action video into short clips, runs a 3D CNN followed by an LSTM, and learns clip-level and sequence-level classifiers within the MultiTL. The authors argue that the clips' loss sharpens temporal attention and stabilizes training, so MultiTL is not decorative but fundamental. Reference [64] explores the same idea, however, in a domain-action-quality assessment in competitive diving, showing that the auxiliary tasks provide important semantic details. Other studies treat MultiTL as a tool for cross-domain generalization. Reference [65] couples RGB and depth footage drawn from different datasets. Here, a sparsely regularized transfer layer learns a shared embedding, while separate classifiers protect each domain from negative transfer. Similarly, [66] shares early filters between smartphone activity recognition and phone-position detection, showing that invariant inertial cues emerge only when tasks are trained together. In these works, multitasking is a tool, rather than the main focus, yet ablation studies show that the networks would not provide as satisfying results without it. In this period, the most explicit attempt at managing tasks is from [67]. A controller updates the relative weight of identity recognition and activity classification modules every few iterations, marking a work where video MultiTL allows the optimizer itself to decide how much an auxiliary task should matter during training. Furthermore, sample-specific weighting appears in [68], where a gating module examines each video and predicts how strongly RGB and optical-flow features should contribute to the final label. The gate's outputs vary across samples, replacing the one-weight-fits-all practice; however, the gate's loss is carried by a fixed coefficient. Early recognition also benefits from MultiTL, still mostly as a tool, rather than the main focus. Reference [69] adds a progress bar that estimates how much of a gesture has unfolded, while [70] couples pedestrian detection with a time-to-cross regressor for autonomous driving. The novelty of these papers lies in the anticipation metric, while the joint optimization constitutes the mechanism that aligns temporal and spatial cues. Skeleton-centric research matures in [71], which streams joint positions, bone orientations, and temporal movement through pairwise multi-task heads. This arrangement boosts accuracy without inflating parameters, again illustrating that MultiTL can serve as a fusion mechanism, without being the main focus.
Reference [72] extends this idea to egocentric video, adding hand-location and gaze-point regression so that the action classifier attends to the wearer's manipulations.

Works in 2019 show a subtle transition, towards questioning how tasks should influence one another and, in some cases, allowing the model to learn that influence. Cross-domain sharing is addressed as a means of significant knowledge exchange even among seemingly disconnected domains, and the first weight adaptation proposals appear. However, the field still has steps to take to reach a dynamic, data-aware optimizer. Task relationships are either assumed or imposed, and it remains to identify through this survey whether this limitation persists.

## D. SELF-SUPERVISION AND LOSS EVOLUTION

2020 extends MultiTL into self-supervision, remaining useful and effective, but does not yet deliver the fully dynamic, iteration-aware weighting that was observed to be an open gap in earlier years. Reference [73] extends the idea of [74] by combining eight self-supervised objectives and then uses an evolutionary search to find suitable loss coefficients. Both works place MultiTL at the center of design, but their self-supervision only reduces the need for labels rather than removing them. The rest of the studies that year treat MultiTL mainly as a practical tool for new sensing domains. Reference [75] tackles EMG gestures and adds a force-estimation head that can be switched on or off during training to handle style differences. In surgical robotics, [76] joins gesture recognition with an automatically generated progress score, which helps curb over-segmentation. Reference [77] shares early CNN-GRU layers across smartphone users while keeping user-specific heads separate, improving personalized activity recognition. Finally, the Collective-Sports benchmark introduced in [78] assigns the labels of “sport” and “collective action” to every clip, creating one of the first benchmarks specifically made for MultiTL. The authors here show that even a simple MultiTL baseline beats STL models, showing that the dataset itself encourages a MultiTL approach.

More recent works lean towards a more architectural question of “how MultiTL can impose the structure that raw video lacks, while keeping models compact enough for utilizing them?”. A way to handle this problem through MultiTL is by turning every variation within these raw videos into tasks. That way, through the joint training of these tasks, the network is somewhat forced to discover their common ground, even when the labeled data are scarce. [79] deployed this idea, where in aerial-manoeuvre recognition, drone video segments were jointly used to train a network with game-engine renderings and GAN hallucinations, with each one of those streams treated as a task, so that they regularize the small target domain. An idea related to this is to distill motion inside the network, through hallucinated features of a learner, while it learns to perform classification [80]. Adversarial self-distillation

in [81] goes one step further, with a teacher network supplying pseudo-labels, a student learning to match them, and a critic grading their agreement, creating an endless loop of auxiliary targets that minimizes the annotation cost. This year also shows MultiTL becoming a tool for handling skeleton and pose streams. Joint positions, bone orientations and higher-order derivatives are embedded in twin or triple branches [82], [83], with triplet or cross-reconstruction losses tying them together. Much like [82] and [83], which use complementary skeletal signals to make the representation more solid, [84] brings time into the discussion. Here, the model must predict the future joint path as a second task, and the concurrent action loss keeps those forecasts consistent, reducing the autoregressive drift that typically accumulates in unconstrained motion predictors, while on the RGB side, an end-to-end pose and action pipeline [85] uses pose loss as a network regularizer. Across these works, the geometry heads confirm that MultiTL has evolved a joint training approach to a solid way to take advantage of missing priors. An important step in the evolution of human action recognition, with the joint use of MultiTL - comes through the first proposal of a hierarchical dataset of event-sets-elements that maps human movement into a triple-task MultiTL setup [86]. The authors propose a SlowFast-style backbone with three parallel paths, each trained on one level of the hierarchy, and show that sharing early visual features while keeping level-specific heads improves recognition at all three tiers. Following the same pattern of “granularity helping the network perform better,” [87] and [88] also show that surgical gesture datasets profit when coarse phases supervise fine steps. Driver monitoring in [89] follows a similar idea, where a clean frame, an illumination-normalized variant, and a shuffled negative pass through an encoder, forcing global body layout awareness instead of background shortcuts. MultiTL in that period further extends from the hierarchical organization of data to temporal precision. In action detection, [90] adds a boundary-confidence head so the network simultaneously classifies and sharpens start-and-end points, while [91] couples a proposal generator with the detector itself, letting errors in one task regularize the other. These studies show that MultiTL is no longer just an optional extra tool, but has become the main framework that allows a model to handle information at many levels, from broad phases, such as whole activities, to tiny gestures, and make them work well together, effectively exchanging knowledge.

In 2022, the works that emerged are observed to be quite diverse, probably due to the vast development of different new DL approaches, such as the generative models that made their impressive appearance through different generative models that were employed. On one side, data remained scarce in some fields where human action recognition is important, due to confidentiality issues, availability of subjects, etc., while at the same time, a battle of generating realistic human motion data followed the generative models trends. At that point, the idea of human movement complexity becomes more
specific, as the hunch that even different movement subtasks
can actually share information is common in research.
Different hand shapes, different trajectories, and modalities
can actually share the same information, only in finer or
coarser levels. Reference [92] fits this intuition into a work
on loss for 3D hand pose, blending surface, 2D reprojection,
and 3D joints, in a way so that each one of those tasks
controls in some way the others. Reference [93] turned
continuous inertial gestures into a dual problem, trying to
reply to the questions “where is a gesture’s key interval” and
“what gesture is that”, proving that temporal localization and
class identity regularize each other once trained jointly. At a
coarser level, [94] proved that grasp intent and contraction
force are not separable signals but complementary facets
of forearm EMG, which, when predicted synchronously,
reduced gesture error by nearly a third without extra sensors.
Thus, this consists of a path where the goal is not to find a way
to generate huge amounts of data for training, but to achieve
satisfying results from the available data, even when scarce,
through knowledge exchange. Following this idea, domain
adaptation work such as in [95] used MultiTL as a surrogate
for scarce labels, rather than an end in itself.

One of the standard approaches, generally in MultiTL and specifically in human movement recognition, concerns the use of a single backbone with shared layers, followed by task-specific modules that solve each task separately after exchanging knowledge. However, as mentioned earlier, sharing not only the same layers but also the same weights can actually lead to negative transfer. Reference [96] identified this problem in self-supervised video training, and in order to avoid negative transfer, let each task-specific head extract its own features first and put them into shared layers only towards the end, so that each task would take advantage of important knowledge after it has learned its own specific characteristics. The authors in [97] followed the opposite route with MuMu and added an extra activity-group task that acts like a gate, deciding when and how information flows to the main action recognizer. Instead of separating tasks at the beginning and only getting them together at the end, they used one to guide the other. In a sports-coaching model, [98] kept one shared convolutional layer but gave each limb its own regressor and loss. That way, fine limb details stayed clear of whole-body noise. A different twist comes from [99], where Wi-Fi channel state data are compressed through a vector quantized autoencoder whose latent code must satisfy both a reconstruction head and an action recognition head. By forcing the same code to serve these two, the backbone effectively becomes a physics-aware compressor rather than a mere feature extractor.

E. FROM ASSUMED TASK-RELATEDNESS TO
OPTIMIZATION DYNAMICS

On to the topic of loss weighting, although many works
on MultiTL still sum task losses using equal or fixed
weights, some works on human movement recognition

start letting the model decide where to focus its attention. Reference [100] applied homoscedastic-uncertainty theory, so that their network could adjust, step by step, the relative importance of motion-direction, motion speed, and reconstruction errors. The adaptive trade-off allowed their unsupervised skeleton tracker to outrun prior baselines. Reference [101] aiming through CogAx at cognitive impairment screening, blended a contrastive embedding loss with two orthogonal classification losses, and their monitoring showed that easing off the contrastive term early in training gave the harder impairment classifier room to improve, providing an example of a loss acting as a support for another. A similar observation came up in [95], when the authors supplemented the categorical cross-entropy with a triplet loss, the features generalized to unseen subjects, significantly improving recognition accuracy in several cases. At that point, preventing catastrophic forgetting, while still adapting to new data, became the main goal. Reference [93] recast gesture detection into two one-dimensional heat maps rather than many spatial anchors, trimming per-frame computation yet retaining the benefit of joint detection classification gradients, thus meeting smartphone timing budgets. Aiming at prosthetic control, [94] restricted sharing to the earliest Batch Norm and Dropout layers, which kept LSTM-based recognition under thirty milliseconds, proving that the gain from MultiTL does not require sharing every weight in the network across all tasks. However, loss-weight selection still leans heavily on empirical tests and hand-tuning, despite a few encouraging uncertainty-weighting schemes. Empirically effective results often surpass theory, meaning there remains no clear explanation for why particular tasks help each other, while others lead to negative transfer, as highlighted by [102]. As a result, the community seems to have identified the need to work deeper in optimization dynamics and understand what binds tasks together.

By 2023, most approaches appear to work under the
assumption that tasks such as gesture recognition and sign
language translation are naturally related. This assumption is
visible in [103], which uses a shared encoder-decoder struc-
ture to perform gloss recognition and sentence translation,
arguing that both tasks benefit from the same latent video
features. The paper explores both hard and soft parameter
sharing strategies and observes a performance benefit with
hard parameter sharing, but does not quantify the relatedness
of these tasks across training. As such, it reflects a trend
where task combinations are assumed to be compatible
but rarely validated or monitored for negative transfer.
Similarly, in [104], recognition and translation tasks are
paired in a multimodal learning setup, using a cross-modal
attention network. Task-relatedness is also assumed, but
without a theoretical or empirical decomposition of whether
knowledge from one task helps the other during training.
The same holds for [105], where emotion recognition, action
recognition, and anomaly detection are learned jointly in
a hard parameter-sharing architecture, with the benefit of
shared representation again observed empirically. These
modalities likely have a cognitive relationship, however, without proof that their signals are tightly related and can provide important knowledge to each other. Reference [106] formulates a more task-aware fusion mechanism, particularly for egocentric and exocentric viewpoints, proposing a feature disentanglement module that aims to separate shared from private task information.

Across these recent works, loss function design remains a bottleneck for innovation. The dominant approach continues to be the simple summation of task-specific losses, often with equal or fixed weights, regardless of task difficulty, convergence behavior, or data imbalance. For example, [107] introduces a dual-stream attention network for egocentric and exocentric views but sums the loss of both views and tasks, assuming their contributions are symmetrical. No mechanism is introduced to re-balance or adapt the losses dynamically based on learning signals as training evolves. Reference [108] shows a shift towards that direction. Although loss summation is used, attention is paid to pose modality supervision, and the network is explicitly penalized for pose estimation and action recognition together. This suggests an implicit awareness that some auxiliary tasks can regularize the main task, but even here, weight tuning remains manual, not learned. No meta-objective is introduced to guide the loss dynamics over time. In [109], there is an attempt to mitigate static loss summation by using contrastive losses to align pose representations across modalities, which helps guide the fusion of RGB and skeleton features. Reference [110] also contributes to this by introducing a multitask variational encoder-decoder structure, where pose and action tasks share parts of the latent space. However, again, loss weights are static, and no adaptation occurs based on learning behavior or feedback. Thus, even in models with conceptual richness, such as [110]'s variational encoding and [106]'s adaptive fusion, the loss architecture remains static, limiting the capacity of the respective models to dynamically prioritize or balance task relevance during training.

Regarding the optimization process, the 2023 models mostly rely on standard stochastic gradient descent variants, without integrating task-level dynamics into them. There is little to no attempt to monitor how gradients from different tasks interact (e.g., conflict or alignment), nor to correct for such conflicts via orthogonalization or task routing methods. [110], for instance, introduces a four-stream saliency-based network for action recognition using RGB, optical flow, pose, and depth. Despite the interesting multimodal approach, the optimization follows conventional procedures with no attention to whether the learning rates or gradient directions across tasks conflict. Focusing on the complex nature of human movement, so far, there are not specific works that focus on the complexity of human movement, as depicted in the introduction. The temporal aspect is partially handled through the chosen DL architectures, but the rest remains underexplored. Some works acknowledge

the complex nature of human movement, particularly in sign language tasks [103], [104], or egocentric views [107], [110], however, the main need is to define and deploy tasks that represent different levels of abstraction, that will be able to represent this complexity.

In 2024, a trend towards multimodality is observed, often without an exploration of how the different modalities complement each other semantically or spatiotemporally. References [111] and [112] work with wearable sensor data, in order to encode spatial dynamics from signals that are usually treated as purely temporal. However, their fusion remains static, and the approaches lack adaptive schemes to prioritize the input tasks based on the task/network needs. On the other side, [113] demonstrates an approach to 3D skeleton-based recognition via graph neural networks. The model attempts to handle view-invariance and multiscale temporal dynamics, and is one of the few works that takes into consideration not only multimodality, but also a multi-perspective alignment. This year, work continues deploying the standard hard parameter sharing setup of MultTL, complementing it with task-specific decoders or heads. References [114] and [115], for example, adopt shared feature extractors, followed by per-task classifiers. This approach can accelerate convergence, but assumes that early representations are universally informative. Soft-parameter sharing is not as widely deployed. One borderline case appears in [116], where the authors employ modality-specific feature branches before merging them via attention — a design which may be interpreted as a weak or implicit form of soft sharing. However, there is no explicit parameter regularization, and the sharing strategy remains hardcoded rather than learned, preventing it from fully embodying the dynamic adaptability that defines true soft sharing. A similar architectural rigidity appears in [117], which fuses inertial and visual features through stacked modules for scene understanding and gesture recognition, but again without adaptive fusion or dynamic weighting mechanisms. None of the above studies integrates optimization-aware strategies despite their relevance to real-world gesture systems where task difficulty fluctuates due to environmental noise or subject variability. This reflects a larger conceptual inertia, where the loss function is treated as a plug-and-play module rather than a tunable interface between tasks.

## F. THE EVOLUTION OF MODALITIES OVER THE YEARS

From 2014 to 2016, the field revolved around two data streams: RGB video with hand-crafted motion descriptors and Kinect skeleton joints. Graph-based MultiTL models such as [36], [37], and [38] partitioned the skeleton into limb tasks, while dense-trajectory features on RGB clips were split across camera views. The simplicity of these streams meant that multi-task experimentation focused on where to draw task boundaries, rather than on heterogeneous-signal fusion. A shift began in 2017, when the two-stream paradigm
<figure><img src="image_7.png" /><figcaption>**FIGURE 5.** Main categories of works on Multi-task Learning on human movement recognition, with the years 2014-2025. The figure highlights the transition from static task coupling and architecture-driven sharing toward adaptive, self-supervised, and multimodal frameworks, revealing emerging trends and challenges in modeling task interaction.</figcaption></figure>

popularized optical flow as a companion to RGB. Multi-task papers like [49] turned flow from an input into an auxiliary objective, asking the network to learn motion cues instead of pre-computing them. Skeleton research moved from fixed limb graphs to latent “skeletons” that emerge inside the optimizer [53], and multi-view RGB footage became its own set of tasks [50], [51]. By 2018, purpose-built auxiliaries had become the norm, each mirroring a missing or expensive modality. Flow prediction [55] and 2D/3D pose regression [56] served as internal teachers for RGB backbones. Depth returned as a view-invariant partner to RGB in [65], while egocentric cues—hand masks and gaze points—guided attention in [72]. The first on-body IMU experiment [66] showed that sensor-position recognition could regularize inertial action features. Cross-domain sharing revealed that merging such streams can lead to interference, motivating a need for adaptive task weighting. The 2019 corpus mainstreamed this diversification and added entirely new channels. Reference [67] introduced radar

micro-Doppler signatures, coupling identity and activity tasks through a learned loss-weight controller. [68] let a gating module choose, per video, how to mix RGB and optical-flow features—marking the first sample-level fusion strategy. Skeleton work built up in a tri-stream design that processed joints, bone orientations, and temporal displacements concurrently [71], while anticipation heads in [69] and [70] turned progress estimation itself into a supervisory signal.

In summary, modalities expanded in layered waves—RGB to Flow to Skeleton/Depth to Egocentric/IMU to Radar—each wave redefining what counts as a “task” and exposing new forms of task interference. Early studies could afford static loss weights because streams were homogeneous, however, later work had to confront the harder question of how to fuse and balance heterogeneous data. By 2019, MultiTL was no longer just a way to re-use a backbone: it had become the primary mechanism for coordinating a growing family of sensing modalities, even if the community was
just beginning to explore how to let that coordination evolve during training.

## G. COMPARATIVE EVALUATION OF MULTI-TASK ACTION RECOGNITION ON PUBLIC BENCHMARKS

For the sake of comparison, there are many works that test their methodology using MultiTL for human action recognition on public human motion datasets, such as UCF101, HMDB-51, NTU RGB+D 60, and NTU RGB+D 120, which are the most used public datasets in this field. Tables 1, 3, 2 show the accuracy results for those datasets, as grouped from the papers on the MultiTL subject in this survey.

The UCF101 is a dataset collected from YouTube, consisting of 101 action categories and 13.320 videos, attempting to provide data with a large variation in illumination condition, camera motion, and object scale [118]. As given by the creators of the dataset, the samples can be divided into five category types, human-object interaction, body-motion, human-human interaction, playing musical instruments, and sports.<fnref n="1" />

The HMDB51 dataset includes 7,000 videos from 51 action classes, collected from different sources, such as movies, archives, videos from Google and YouTube [119]. Its creators also selected video samples that represent actions with different perspectives, light conditions, and backgrounds, in order to create the challenging conditions required for testing DL networks.<fnref n="2" />

The NTU RGB+D family consists of the NTU RGB+D 120 and NTU RGB+D 60 datasets, which are two of the most used datasets in the field of human action recognition [120]. The NTU RGB+D 60 comprises of 60 action classes and 56.880 samples, while the NTU RGB+D 120 is an extension of the first dataset, with 120 action classes and 114.480 samples in total of RGB videos, depth map sequences, 3D skeletal data, and infrared videos for each one of the samples.<fnref n="3" />

Concerning the grouped results in Table 1, the results improve over the years for the 3-split average, with improvements of roughly 3 to 4%. In this Table, the highest accuracy results are provided by the same works as in Table 2, for the HMDB51 dataset; however, the improvement over the years in this case is much higher, reaching up to +22%. In an attempt to identify the strong points of the three works [55], [68], [74], they are compared and further analyzed. [74] reaches the highest score on UCF101 with a 3-split average accuracy of 98.7%, after the network is pretrained on Kinetics and then fine-tuned with both RGB frames and optical-flow stacks as inputs (Figure 6). Rev2Net trains a single 3D CNN that, besides naming the action, must predict the optical flow of each frame pair and reconstruct the same clip in reverse.

Both auxiliary decoders plug into the shared backbone, so their gradients teach the backbone to capture motion information automatically. A Decoding Discrepancy Penalty keeps the two decoders in sync, preventing them from drifting towards different features.

The exact same approach, with the use of ImageNet for pre-training, provided the third-best accuracy of 97.1%, thus showing that the pre-training dataset actually matters. Even though ImageNet is numerically larger, with more than one million training images, Kinetics provides 240.000 training videos, reaching in raw frames, more than 60 million images, with every video having body motion, camera movements, scene transitions, all of which benefit human action recognition. Accordingly, for the HMDB51 dataset (Table 2), the same approach achieves the best results when pre-trained with the Kinetics dataset, with 81.9%, and the second best in the case where the ImageNet is deployed, an average 3-split accuracy of 78.0%. Apart from this, it is important to observe that Rev2Net's approach provides with very high accuracy on UCF101, compared to the HMDB51 dataset, which could probably be due to the nature of the pretrained dataset, Kinetics, whose style and action labels more closely resemble the style of UCF101, but not the ones from the HMDB51 dataset, where the videos include abrupt transitions and zooming that can actually confuse optical-flow estimation and disrupt Rev2Net's auxiliary tasks far more than the single-take motion typical of UCF101. So while both datasets share a YouTube origin, UCF101's curated, action-centric snippets present cleaner motion statistics, whereas HMDB51's sequences make the self-supervised flow and reverse-reconstruction objectives noisier and less helpful. Focusing on the work of [68], DynamoNet learns by coupling action classification with a self-supervised auxiliary task that predicts the next frame through video-dependent dynamic motion filters (Figure 7). Both tasks share the same 3D STC-ResNext backbone, so the frame prediction loss injects fine-grained motion information directly into the features that the classifier uses, overcoming the need to pre-compute optical flow while still capturing its benefits. This joint optimization results in 97.8%, 3-split average accuracy on UCF101 and 76.8% on HMDB51. Among the MultiTL approaches, [68] and [74] distinguish themselves because the auxiliary task is co-trained within the backbone and pushes it to model motion explicitly, while the other frameworks add supervision only on top of frozen or loosely coupled features. More specifically, the “super-category” regularizer of [43] groups action labels through affinity propagation and imposes block-sparse weights on linear SVM heads, improving sample efficiency on hand-crafted Fisher Vectors, but never touching the video backbone. Reference [110] adds saliency-guided spatial and temporal streams and shares an LSTM through a four-way loss; however, the streams are still processed independently before fusion, leaving the base CNNs unaware of each other's tasks. Lastly, results in both [58] and [63] show that alternating captioning and classification for the first work, or weighting clip-level tasks

<footnote n="1">¹https://www.crcv.ucf.edu/data/UCF101.php</footnote>

<footnote n="2">²https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/#dataset</footnote>

<footnote n="3">³https://rose1.ntu.edu.sg/dataset/actionRecognition/</footnote>
<figure><img src="image_8.png" /><figcaption>**FIGURE 6.** A schematic representation of Rev2Net that achieved the best and third best results for the UCF101 and HMDB51 datasets in MultiTL [74].</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**FIGURE 7.** A schematic representation of DynamoNet that is among the best results for the datasets UCF101 and HMDB51 [68].</figcaption></figure>

for the second one, regularizes training, but without an encoder-level factor, they inherit the limitations of the two-stream architectures. Overall, the strongest results so far come from approaches in which an auxiliary task actively shapes the backbone features. Classical MultiTL regularizers still improve sample efficiency, but they generally do not reach

the top accuracies achieved by these newer self-supervised designs.

Focusing on the 1-split ("top-1") results on the same datasets, every method in Tables 2 and 1 uses MultiTL to provide motion cues into a single recognition backbone, although they differ in what the auxiliary task asks the
<table><caption>TABLE 1. Comparison of methods on multi-task learning. Accuracy on the UCF101 dataset, either 3-split or 1-split.</caption><thead><tr><th>Split Type</th><th>Method</th><th>Year</th><th>Pre-training</th><th>Backbone</th><th>Accuracy</th></tr></thead><tbody><tr><td rowspan="13">3-split</td><td>Gated TSN [60]</td><td>2018</td><td>ImageNet</td><td>TSN</td><td>94.5</td></tr><tr><td>C3D+DML+LWF [58]</td><td>2018</td><td>Sports-1M</td><td>C3D</td><td>82.8</td></tr><tr><td>DynamoNet (ResNext) (16 frames) [68]</td><td>2019</td><td>Kinetics</td><td>STC-ResNeXt-101 3D CNN</td><td>91.6</td></tr><tr><td>DynamoNet (ResNext) (32 frames) [68]</td><td>2019</td><td>Kinetics</td><td>STC-ResNeXt-101 3D CNN</td><td>93.1</td></tr><tr><td>DynamoNet (STCnet) (32 frames) [68]</td><td>2019</td><td>Kinetics</td><td>STC-ResNeXt-101 3D CNN</td><td>96.6</td></tr><tr><td>DynamoNet (STCnet) (64 frames) [68]</td><td>2019</td><td>Kinetics</td><td>STC-ResNeXt-101 3D CNN</td><td><strong>97.8</strong></td></tr><tr><td>Multi-task C3D+LSTM [63]</td><td>2019</td><td>Sports-1M</td><td>C3D</td><td>93.4</td></tr><tr><td>Rev2Net (RGB+Flow) [74]</td><td>2020</td><td>ImageNet</td><td>3D-ResNet-50</td><td><strong>97.1</strong></td></tr><tr><td>Rev2Net (RGB+Flow) [74]</td><td>2020</td><td>Kinetics</td><td>3D-ResNet-50</td><td><strong>98.7</strong></td></tr><tr><td>STSF [110]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>93.2</td></tr><tr><td rowspan="4">1-split</td><td>ActionFlowNet (multiframe) [55]</td><td>2018</td><td>UCF101</td><td>3D-ResNet-18</td><td><strong>83.9</strong></td></tr><tr><td>HalluciNet [80] single frame input</td><td>2021</td><td>ImageNet</td><td>VGG-11-bn</td><td>70.5</td></tr><tr><td>Wang et al. [96]</td><td>2022</td><td>Kinetics</td><td>C3D</td><td>80.4</td></tr><tr><td>Wang et al. [96]</td><td>2022</td><td>Kinetics</td><td>R(2+1)D</td><td>81.6</td></tr></tbody></table>

<table><caption>TABLE 2. Comparison of methods on multi-task learning. Accuracy on the HMDB-51 dataset, either 3-split or 1-split.</caption><thead><tr><th>Split Type</th><th>Method</th><th>Year</th><th>Pre-training</th><th>Backbone</th><th>Accuracy</th></tr></thead><tbody><tr><td rowspan="13">3-split</td><td>Yang et al. [43]</td><td>2016</td><td>No</td><td>IDT + Fisher Vector</td><td>60.8</td></tr><tr><td>C3D+DML+LWF [58]</td><td>2018</td><td>Sports-1M</td><td>C3D</td><td>52.6</td></tr><tr><td>DynamoNet (ResNext) (16 frames) [68]</td><td>2019</td><td>Kinetics</td><td>STC-ResNeXt-101 3D CNN</td><td>66.2</td></tr><tr><td>DynamoNet (ResNext) (32 frames) [68]</td><td>2019</td><td>Kinetics</td><td>STC-ResNeXt-101 3D CNN</td><td>68.5</td></tr><tr><td>DynamoNet (STCnet) (32 frames) [68]</td><td>2019</td><td>Kinetics</td><td>STC-ResNeXt-101 3D CNN</td><td>74.9</td></tr><tr><td>DynamoNet (STCnet) (64 frames) [68]</td><td>2019</td><td>Kinetics</td><td>STC-ResNeXt-101 3D CNN</td><td><strong>76.8</strong></td></tr><tr><td>Multi-task C3D+LSTM [63]</td><td>2019</td><td>Sports-1M</td><td>C3D</td><td>68.9</td></tr><tr><td>Rev2Net (RGB+Flow) [74]</td><td>2020</td><td>ImageNet</td><td>3D-ResNet-50</td><td><strong>78.0</strong></td></tr><tr><td>Rev2Net (RGB+Flow) [74]</td><td>2020</td><td>Kinetics</td><td>3D-ResNet-50</td><td><strong>81.9</strong></td></tr><tr><td>STSF [110]</td><td>2023</td><td>Kinetics</td><td>ResNet-50</td><td>65.7</td></tr><tr><td rowspan="4">1-split</td><td>ActionFlowNet (multiframe) [55]</td><td>2018</td><td>UCF101</td><td>3D-ResNet-18</td><td><strong>56.4</strong></td></tr><tr><td>HalluciNet [80] single frame input</td><td>2021</td><td>ImageNet</td><td>VGG-11-bn</td><td>39.4</td></tr><tr><td>Wang et al. [96]</td><td>2022</td><td>Kinetics</td><td>C3D</td><td>46.1</td></tr><tr><td>Wang et al. [96]</td><td>2022</td><td>Kinetics</td><td>R(2+1)D</td><td>46.1</td></tr></tbody></table>

network to do and how closely it is coupled to the classifier. The work that results in the best accuracy [55] treats optical-flow estimation as an auxiliary task. A 3D ResNet first predicts frame-to-frame flow for a 16-frame clip-plus the flow “into” the unseen 17th frame, while a parallel head classifies the same clip. Thus, the shared layers have to organize features that are simultaneously useful for dense motion regression and action labels. On the other side, the prior-guided contrastive learning in [96] provides a semantic structure, however, it leaves the temporal relationships implicit, while [80] shows that MultiTL can compensate for missing motions to a degree, though without any temporal evidence at training time.

Another dataset frequently used across works was the NTU RGB+D 60, where both cross-view and cross-subject results were gathered. The works with the best results in the cross-view setup were those of [82] and [113], reaching accuracies of 95.0%, 97.2%, and 96.1%, respectively. In PGEN with body partitioning (w/ partition), two independent convolutional trunks learn physical cues for the joints and geometric cues for bone orientation, with each trunk outputting its own softmax and together with a third softmax fed by their concatenation, form

a three-task loss, whose backpropagation forces the trunks to have a common embedding space. Ablation studies show that removing this multi-task coupling costs almost two accuracy points, showing that complementary tasks enrich the shared representation. Reference [83] pushes the same principle further. A view-adaptive layer first rotates every skeleton into a standard pose, then a multiscale graph stack builds joint, part, and temporal features. One head labels the action, while the other forecasts how the joints will move in the next few frames, and both heads work with the very same graph features. The extra motion-prediction loss teaches the classifier to notice changes in speed and direction that plain cross-entropy overlooks, lifting accuracy to 97.2%. When that branch is removed the score falls to 96.1%, showing that the architectural graph design already helps, but the auxiliary prediction still matters. However, even though the result is decreased by around 1%, it remained at the top of the accuracy list for the cross-view setup. The rest of the MultiTL methods in Table 3 do not let their tasks mix as much. For example, Clips+CNN+MTLN [49] classifies four clip frames in parallel and only averages the four scores at the very end, so each branch works in isolation.
<figure><img src="image_10.png" /><figcaption>FIGURE 8. A schematic representation of the proposed MultiTL approach for human pose estimation and action recognition from [85] (top) and the proposed MultiTL architecture of the same work (bottom).</figcaption></figure>

On the cross-subject side, every movement has to be recognized on bodies that the network has never seen before, thus making the task of recognition more difficult, so within the works in Table 3, only three of them reach a 90%-and-up accuracy. In the work of [90], each skeleton clip is sliced into four reference joint frames, which are classified in parallel, and their losses are summed, so that the backbone is forced to learn features that serve several viewpoints of the same motion at once. Here, the supervision comes before the final fully-connected layer. Gradients from all tasks shape the shared CNN filters and help the network ignore details such as the clothing color, leading to a higher accuracy compared to the rest of the works in the cross-subject setup. On the other hand, PGEN-fusion [82] deploys two CNN branches, which are given as input joint positions, velocities, accelerations, bone vectors and orientations, all trained together and optimized with separate losses. During backpropagation, the two branches exchange gradients, so the one learns the “morphology” of the other, leading to one of the top accuracies in Table 3. Reference [90] four-way classification, [82] physics-geometry duo, and [113] recognition and prediction pairs reshape through the gradients the very filters that encode motion, so the network learns features that can generalize instead of focusing on “memorizing” specific body differences.

In NTU-RGB+D 120, two evaluation splits are used for evaluation in different works, as shown in Table 3, a cross-subject and a cross-setup one. In the cross-subject one, the network is trained on the motions of half of the dataset actors and tested on the other half, so the network is primarily

tested on changes in different anthropometric characteristics and personal style. In the cross-setup evaluation, all the samples with even collection setup IDs are used for training, and the odd IDs for testing [121]. In that case, Luvizon's MultiTL network [85] has the top performance in the cross-subject setup. An encoder is first trained to predict 2D/3D joint positions, and then the same features feed the action head, so every frame is rewritten in a pose-centric coordinate system before classification (Figure 8). Body joints are less actor-dependent than raw RGB or depth data, thus, this specific representation works much better than the rest in the cross-subject setup. However, the paper does not report cross-setup results, suggesting it may not be the best solution for different setups.

## VI. META LEARNING FOR HUMAN MOVEMENT RECOGNITION

The employment of MetaL specifically for human movement recognition has evolved over the years in an attempt to reflect the complexity not only of the movement scenarios themselves but also the captured modalities (Figure 9). However, although MetaL, as a concept in standard ML, dates back decades, under various names, its strong application to human movement was not observed until 2019 in the presented digital libraries. Since its first use, and even up to current works, the success of this method remains strongly dependent on the deployment of big, well-annotated datasets. However, it has become increasingly evident that the new goal in the field needs to be on developing algorithms that are able to generalize even from limited data and perform well in
<table><caption>TABLE 3. Comparison of methods on multi-task learning. Accuracy on the NTU RGB+D 60 and NTU RGB+D 120.</caption><thead><tr><th>Method NTU60</th><th>Year</th><th>Cross-view</th><th>Cross-subject</th></tr></thead><tbody><tr><td>Clips+CNN+MTLN [49]</td><td>2017</td><td>84.8</td><td>79.5</td></tr><tr><td>Multi-task RNN [57]</td><td>2018</td><td>82.6</td><td>-</td></tr><tr><td>Luvizon et al. [56]<br>(RGB & estimated poses)</td><td>2018</td><td>-</td><td>85.5</td></tr><tr><td>RelaClips+MTCNN [62]</td><td>2018</td><td>84.8</td><td>79.5</td></tr><tr><td>RotClips+MTCNN [62]</td><td>2018</td><td>87.3</td><td>81.0</td></tr><tr><td>3SCNN [71]</td><td>2019</td><td>93.7</td><td>88.6</td></tr><tr><td>Tang et al. [83]</td><td>2020</td><td>-</td><td>87.6</td></tr><tr><td>Mehmood et al. [90]</td><td>2021</td><td>91.4</td><td><strong>91.3</strong></td></tr><tr><td>Luvizon et al. @240fps<br>[85]</td><td>2021</td><td>-</td><td>86.6</td></tr><tr><td>Luvizon et al. @180fps<br>[85]</td><td>2021</td><td>-</td><td>87.7</td></tr><tr><td>Luvizon et al. @90fps<br>[85]</td><td>2021</td><td>-</td><td>89.9</td></tr><tr><td>PGEN (w/o partition) [82]</td><td>2021</td><td>94.9</td><td>89.3</td></tr><tr><td>PGEN (w/ partition) [82]</td><td>2021</td><td>95.0</td><td>89.7</td></tr><tr><td>PGEN-fusion [82]</td><td>2021</td><td><strong>95.6</strong></td><td><strong>90.3</strong></td></tr><tr><td>PDF-G [100]</td><td>2022</td><td>81.0</td><td>59.7</td></tr><tr><td>PDF-G* [100]</td><td>2022</td><td>81.5</td><td>60.4</td></tr><tr><td>Vers-GNN [113]</td><td>2024</td><td><strong>97.2</strong></td><td><strong>91.4</strong></td></tr><tr><td>Vers-GNN (No VAT)<br>[113]</td><td>2024</td><td>94.5</td><td>89.7</td></tr><tr><td>Vers-GNN (No MP) [113]</td><td>2024</td><td><strong>96.1</strong></td><td>90.1</td></tr><tr><th>Method (NTU 120)</th><th>Year</th><th>Cross-subject</th><th>Cross-Setup</th></tr><tr><td>PGEN (w/o partition) [82]</td><td>2021</td><td>83.0</td><td>84.1</td></tr><tr><td>PGEN (w/ partition) [82]</td><td>2021</td><td>81.7</td><td>83.5</td></tr><tr><td>PGEN-fusion [82]</td><td>2021</td><td>83.9</td><td>85.2</td></tr><tr><td>Luvizon et al. [85]</td><td>2021</td><td><strong>89.9</strong></td><td>-</td></tr><tr><td>PDF-G [100]</td><td>2022</td><td>48.2</td><td>50.9</td></tr><tr><td>PDF-G* [100]</td><td>2022</td><td>48.5</td><td>51.3</td></tr><tr><td>Vers-GNN [113]</td><td>2024</td><td><strong>87.6</strong></td><td><strong>88.4</strong></td></tr><tr><td>Vers-GNN (No VAT)<br>[113]</td><td>2024</td><td><strong>86.5</strong></td><td><strong>87.7</strong></td></tr><tr><td>Vers-GNN (No MP) [113]</td><td>2024</td><td><strong>86.8</strong></td><td><strong>87.3</strong></td></tr></tbody></table>

few-shot and zero-shot learning scenarios. This observation came up due to the computational cost of handling large-scale datasets, as well as the computational cost for the generation of new data, in cases where data scarcity is noticed. Landmark frameworks, such as the approach of Model Agnostic Meta Learning (MAML), emerged in 2017 and offered a starting point toward the goal of rapid adaptation; however, they require further architectural adaptations and task-specific fine-tuning in order to be deployed in few-shot or zero-shot cases that are also characterized by the complexity that human movement ones have.

In the sections that follow, the goal is to explore the evolution of MetaL with respect to the models' adaptability and effectiveness in human movement data, existing gaps over the years, and important contributions.

## A. EMERGING PARADIGMS IN META LEARNING FOR MOVEMENT RECOGNITION

The period between 2018 and 2020 represents a foundational stage, with early approaches on MetaL focusing on the dual challenge of limited data availability and the temporal and

user-dependent complexity of human movement. Although MetaL long existed as a paradigm for model generalization and algorithm selection, its major deployment for human movement recognition in the presented digital libraries started by 2019, when the need for data-efficient adaptation met the rising need for vast amounts of data for training human movement recognition models. The earlier works in this period are relatively few, but show a division into two conceptual directions, either as a form of algorithmic reasoning for model selection, using meta-features across sensor channels to pick optimal classifiers [122], or as a task-based few-shot framework intended to generalize to new gestures or users with minimal examples [123]. An observation from this period is the deployment of radar data emerging as the dominant modality in MetaL pipelines, as will also be seen in later years. Unlike RGB video data, radar offers low-cost, privacy-preserving perspectives that are highly attractive for human-computer interaction in real-life scenarios. Both [123] and [124] explore MetaL in radar-based gesture recognition, with [124] proposing a multi-branch architecture that fuses range, Doppler, and angle signals. These works suggest that traditional image-based backbones might not be optimal for sensor-centric tasks, suggesting that MetaL approaches can offer an alternative. However, this kind of data presents practical data limitations, such as limited generalization of radar data in cluttered environments and the computational cost introduced during the pre-processing stage, which all types of data seem to have. The temporal variability of movement seems to be somewhat acknowledged, though not yet fully addressed. Within the earlier two studies, [125] also appears in this window and shifts the focus back to video movement data, emphasizing that one-shot learning on videos is more difficult than static images, since the temporal dimension of video tends to introduce greater variation, thus they introduce an Adaptation-Oriented Feature projection technique, to explicitly fuse the movement characteristics that are more sensitive to adaptation, thereby improving both the quality of the extracted features, and the adaptation process in one-shot action recognition. The authors highlight that embedding-based approaches assume that the learned latent space is robust enough to successfully support unseen class discrimination. This assumption, however, could be weak in dynamic datasets. Thus, their work suggests that future directions should not only aim to refine embeddings but also identify adaptation-sensitive parameters. Despite works with important contributions, the experimental protocols stay focused on publicly available visual datasets, such as the HMDB51 and the UCF101, often without engaging deeply with the embodied or semantic structure of human movement, even though these datasets are the norm for the sake of comparison, so they can not be avoided sometimes. Nonetheless, isolated attempts to go beyond vision-only architectures are also observed. Reference [126] explores external memory networks to store learned experience and enable episodic recall in few-shot gesture recognition. Although centered on a 3DCNN backbone, this work moves toward the idea that
movement understanding may benefit from context-aware
learning architectures, which retain more than just short-term
embeddings. Similarly, [127] proposes a gradient mixing
technique to support task domain adaptation, under semi-
supervised settings. Although not deeply focused on gesture-
specific modeling, this work raises broader questions on
how “tasks” are conceptualized, which, up to now, has been
done in general terms, without reference to the functional
structure of human movement. This remains an open question
to be answered further in the MetaL for human movement
exploration. In short, the period from 2018-2020 represents
a foundational phase in the modern MetaL literature for
human movement recognition. The contributions are limited,
but open discussions about the need for methods that can
generalize across time, users, and tasks, while the complexity
of human movement remains underexplored. Through the
years 2021 to 2023, the scientific community working on
MetaL for human movement recognition expanded and even
though the main goal remains few-shot human movement
recognition, there is an evolution from “5-shots are enough”
to a further question of what is the knowledge among
new users, new environments, and new sensors that would
not harm the learning process, but rather increase its
generalization capabilities? This question is progressively
unfolded within the next sections, each of which naturally
blends into the next, rather than replacing it.

B. REPRESENTATION AND ADAPTATION COUPLING

Studies in different modalities, ranging from electromyogra-
phy and body-worn IMUs to Doppler radar, Wi-Fi Channel
State Information, as well as conventional video, converged
on the same insight, that the design of an encoder and the
MetaL architecture that makes that encoder adapt cannot be
independent of each other. Electromyography studies initially
show this coupling, as they observed that swapping a fully
connected encoder for an LSTM or other temporal blocks
changed how well the inner loop of MetaL compensated
for user-specific signal changes, confirming that rapid
adaptation must be aligned with the features it has to fine-
tune [128], [129], [130]. IMU research in clinical studies
reaches a similar conclusion, where the error significantly
decreased when attention layers and triplet multi-task losses
were jointly optimized [131], while works deploying Wi-Fi
data arrived at the same observation, by first compressing
features through a CNN-LSTM module and then using a
metric-based meta-update to recalibrate those features in
unseen positions [132]. Research on video and radar data
also echoed this point. Reference [133] paired lightweight
comparison heads with meta-updates, letting users define
new gestures on the fly, something that a static encoder
would not be able to do, while [134] further building
on this foundation fused range, Doppler and angle cubes
in a dual-channel 3DCNN, showing that only a learnable
relation module inside the meta loop could exploit those
richer features. Reference [135] extends this trajectory by

coupling an autoencoder with MAML and gradient-stability
constraints, in order to compress the model, while still
enabling sub-second adaptation, something that would not
be feasible with a frozen backbone. The same principle is
also adopted for video modalities, where [136] transposes
the same meta-learning coupling to RGB data, replacing an
inner-product matcher with a cosine attention metric during
training and retaining cosine scoring at inference. The above
works reinforce the initial observation that reliable human
movement recognition in a few-shot setting requires encoders
that are co-designed with the MetaL framework, whether
employing Doppler shifts or full body action in video streams.
With the available effective backbones now being plenty, the
goal of the scientific community is slowly shifting from data
quantity to data distribution. Research works in the domain
of MetaL for human movement recognition initially tried
to pull domains together through contrastive and invariant
objectives, such as unsupervised video pretraining [137],
SEEN for cross-domain actions [138]. These advances, prob-
ably along with the pandemic-era deployments, which accel-
erated non-contact sensing technologies, explain MetaL's
emergence in 2022.

C. STRUCTURE AWARE AND DOMAIN SENSITIVE META LEARNING

Between 2022 and 2023, MetaL was widely deployed,
and the community of human movement recognition and
analysis moved toward focusing on the structure of the
movement itself. Metric-based models with implicit temporal
alignment [139], task-adaptive relation networks [140], and
relation-guided prototypes [141] treated time as a modeling
primitive. Further research on skeletal data was made
more extensive, with ALCA-GCN adapted local-component
graphs [142], part-aware prototypes that capture subtle intral-
imb cues, and dual-representation networks fusing body-part
dynamics with global relational graphs for fine-grained trans-
fer [143], [144]. Radar work mirrored the trend with range-
angle fusion [134] and cross-frequency adaptation [145],
while omnidirectional micro-Doppler models injected view-
conditioned perturbations to stabilize similarity across
angles [146]. Transformer embeddings appeared in meta-
loops, where video transformer features inside ProtoNets
surpassed the results of those of a CNN, after self-supervised
pre-training [147], and reinforced frame-promotion modules
learned to ignore redundancy before adaptation [148].
However, apart from this shift to the human movement
structure, the field still misses important consideration of
the real complexity of human movement and its various
characteristics.

At that point, “domain” became a common word within
the human movement community and, somehow, stopped
being a synonym for “dataset” and became the ground that
each paper defines and measures in a different way. During
this period, it became clear that generalization mostly fails
due to changes in the data distribution, and not because
the deployed sample is small. The notion of “domain” in these works is not singular, and can refer to different datasets, such as in [136], where the DL model was trained on Kinetics, but classified actions in UCF-101, HMDB51, and SSv2. Reference [137] treats the subject in a similar way, where each dataset is treated as a distinct domain, whose distributions must be aligned before few-shot adaptation, while other studies treat each subject or device-specific dataset as a domain, to capture the unique characteristics that each source introduces, and then align those variations before adapting the model to new tasks.

In sign-language recognition, the domain is connected to morphology, as Reptiles' adaptation treats each hand-shape vocabulary as a separate region of feature space to be crossed in a few gradient steps [149]. Weakly-labeled sensor streams redefine the domain as a noise pattern. In [150], a self-relation attention head first separates signal subsequences from noisy ones and then meta-adapts only the salient parts, effectively hopping between “clean” and “noisy” domains on the fly. In personalized seizure detection, a domain is an individual nervous system. Reference [151] builds patient-wise meta-tasks so that the outer loop learns a “family resemblance” parameter set and the inner loop only has to push it toward one new patient's EEG specificity. Wearable graphs treat the domain geometrically. Zheng et al.'s prototype graph measures not only feature distance but also distribution curvature so that a meta-update can move prototypes along the manifold instead of through Euclidean space [152]. In [153], a multi-level, multi-attention framework names each visual context a domain, letting separate attention heads meta-learn shallow and deep cues simultaneously, while [154] calls each activity scene a “domain” and uses a coarse-fine schedule so that the inner loop first adapts global motion and then local detail. Thus, the above studies characterized the domain as the unit of transfer. Whether the shift is across patients, cameras, or graph manifolds, all methods start by naming the distribution that will move and then building the meta-objective around that movement. Even though there are many new works that deployed MetaL specifically for human movement recognition, two main challenges seem to persist. Temporal reasoning remains underexplored, with only a handful of studies attempting to model long-range dynamics explicitly [139], [140], [155]. As already mentioned, most works, even those deploying richer semantics, still benchmark on public datasets such as UCF101, HMDB51, or Kinetics [135], [136], [156]. This shows the need for new, multimodal, real-world datasets that capture the full variability across both time and space. Ideally, such datasets would combine multiple sensor types (e.g., inertial, vision, depth, and audio) recorded simultaneously during different activities, with dozens of participants with varied anthropometric characteristics and motion styles, and include long, untrimmed recordings to challenge temporal modeling. They should also incorporate controlled domain shifts through the use of different devices, environments,

and subjects, so that the research community can properly evaluate how well models can generalize across new domains. Finally, establishing standardized splits and protocols for few-shot and continual adaptation on these rich, multimodal benchmarks is important for driving progress in MetaL for human movement recognition.

During year 2024, an increase in the work on MetaL for human movement recognition was observed, probably due to the emergence of high-capacity backbones, such as transformers and encoders, which provided a standard starting point and the foundation for further work on MetaL on human movement, without having to also work on representation learning. Metric-based meta-learning dominates, however, with its embedding becoming more task-aware. Prototype-style learning is widely used, yet embeddings are tailored to the movement granularity and its characteristics to be learned. HyRSM++ enriches every clip with an intra-video temporal-relation pass to craft a task-specific embedding, then matches support and query through a bidirectional set-metric before prototype scoring [157], TSA-MLT prunes each clip to task-relevant frames, then feeds multi-scale temporal windows into a transformer to form prototypes for distance matching [158]. Further building on the idea of task-aware embeddings, skeleton-centric work pushes anatomical structure to the center of attention. CD-GCN diffuses attention across joint neighborhoods and couples it with a contrastive loss so that skeletal joints from the same class come closer together, while different classes repel [159]. MRLN [7] introduces a Cross-Video Relation Module that lets each clip borrow context from its support and query neighbors, while DPN keeps relations within a clip, but also sharpens prototypes with class and query-specific spatial attention [160]. Beyond skeletal data, CLIP-initialized pipelines also add movement semantics to the embeddings. TgGTM feeds the CLIP text embedding of each action label into its graph-temporal network so that the video features are guided toward the correct motion class [161], and CLIP-MDMF fuses short-burst and full-clip contexts before prototype matching to balance micro-gestures with overall rhythm [162]. The above studies support the observation that a generic ImageNet encoder is no longer sufficient, and that a feature extractor needs to understand the limb-specific patterns of human motion to be effective.

At the same time, gradient-based Model Agnostic Meta Learning (MAML) variants gain traction in both privacy-sensitive and sensor-stream settings. Concerning the video modality, FedFSLAR++ [163] also relies on Prototypical Networks within a federated loop, but the federated IMU pipelines, such as MSFL [164], [165] and SmartSit's MG-Reptile [166], show that inner-loop gradients can adapt to a new sample much faster than when prototypes are used alone. Works on radar data show the same pattern. AS-MAML [167] feeds micro-Doppler spectrograms through a ResNet-18 backbone and wraps the network in a MAML loop regularized with an angular margin loss, so the
<figure><img src="image_11.png" /><figcaption>FIGURE 9. Main categories of works on Meta Learning on human movement recognition, with the years 2014-2025. The figure illustrates the progression from early few-shot adaptation frameworks toward multimodal and domain-aware approaches, reflecting the emphasis on rapid generalization under data scarcity.</figcaption></figure>

weights can be pushed towards the gait signature of each new pedestrian. This makes the conclusion that when every subject is treated as a new domain, a few inner-loop gradient steps usually help the network adapt much faster than through prototype matching alone, more solid.

Focusing on the privacy-sensitive settings mentioned earlier, federated protocols appear frequently in several studies. FedFSLAR++ [163] and FedFSLAR [168] provide the first cross-backbone, cross-learner benchmarks, concluding that ProtoNet+FedProx is more robust than Reptile under non-independent and identically distributed splits, but collapses without large-scale pretraining. MSFL variants on IMU data show that lightweight CNNs, if meta-trained centrally, can achieve sufficient results in personalization, with only two or three shots [164], [165], even though more works provide results for 1 or 5 shots. These works argue that MetaL is the evident path when the sensors are devices such as a smartphone, a router, or a robot; thus, the data

cannot be placed in a central server for pre-processing and deployment. Privacy can be protected not only by keeping data on the device but also by using sensors that reveal less in the first place. Recent MetaL studies show that non-visual modalities can still reach competitive few-shot accuracy. Wi-Fi CSI systems model augmentation and RoGER's dual-link fusion achieve better one-shot gesture recognition without a camera in sight [169], while acoustic ultrasound data in PreGesNet pushes one-shot accuracy higher with just a second example (two-shot) by modulating FiLM layers for each new gesture [170]. FMCW radar goes further: AS-MAML adapts a ResNet-18 to a new walker's micro-Doppler signature, reaching roughly 83% after a single support clip and over 94% in five-shot tests [167].

Moving on to 2025, representation enrichment has begun to replace blind similarity. Whether via diversity penalties (ESR-MM), global action dictionaries (RFPL), or Transformer augmentations (TACO), the field is moving beyond
plain feature averaging. Domain shift is now treated as the MetaL target. Wireless and wearable datasets push this further, and cross-domain video benchmarks make it more concrete. Computational caution is no longer an afterthought. SDCOM [171] shows that meta-learners can manage FLOPs budgets dynamically instead of relying on fixed pruning schedules. Prototype-style metrics still dominate, but hybrid strategies emerge. Distillation (DMSD) [172], reinforcement learning (RFPL), and graph propagation (RoMF) [173] hint at richer adaptation mechanisms. Regarding the evaluation schemes, most studies still report 5-way/1- or 5-shot accuracy, while only a few quantify energy or memory cost on real wearable devices. This suggests that the community needs unified benchmarks that include RGB, skeleton, wireless, and IMU signals, while lighter graph backbones and self-supervised encoders could help explore further multimodal fusion, without sacrificing the speed of results. Finally, hybrid meta-learners that combine prototypes with fast gradient adapters or episodic memories may be the key to handling large daily-action vocabularies.

## D. COMPARATIVE EVALUATION OF META LEARNING FOR ACTION RECOGNITION ON PUBLIC BENCHMARKS

For the sake of comparison, many works evaluated their results on public datasets, with the most used ones, under comparable evaluation settings, being again the UCF101 and the HMDB51, as well as the Something-Something v.2 dataset, and the Kinetics-100. The contents of the first two datasets have been summarized earlier, in the MultiTL section, while regarding the latter two, the Something-Something v.2 (SSv2) dataset consists of 77.500 videos for training across 100 classes, with 1926 videos for validation, and 2854 for testing purposes [174]. The Kinetics-100 dataset has 64 action classes with 6.389 videos for training, 12 action classes with 1199 videos for validation, and 2395 videos from 24 action classes for testing [7].

Starting with the UCF101 dataset, through the years, different backbones and pre-training datasets have been deployed in the MetaL works for human action recognition, with ImageNet being the most used pre-training dataset and the ResNet-50 the most deployed backbone, probably due to the fact that ImageNet offers a very large and diverse collection of labeled images that cover a wide variety of shapes and lighting conditions. As for the backbone, ResNet-50 tends to strike a strong balance between computational efficiency and representational power. However, the works that achieved the best results for human action recognition haven't deployed either the ImageNet or the ResNet-50. More specifically, the top 3 performances, both for the 1-shot accuracy and the 5-shot one, were relatively new, published in 2024. All three works are within the same accuracy range, varying from 97.8% to 97.0% for the 1-shot and 99.6% to 99.3% for the 5-shot accuracy. Notably, CLIP-MDMF [162] starts from a vision-language foundation model. Instead of a 2D ResNet pretrained on ImageNet, the authors fine-tune

the CLIP encoder jointly with a cross-transformer that fuses global and local temporal views, and then distills reliability between the two. CLIP's multimodal pre-training provides action semantics that standard ImageNet pre-training does not capture, and the cross-view distillation lets the few-shot learner exploit those semantics even when only one support clip is available.

Reference [175] and its Chamfer+TFRS variation reshapes the support features before matching in every step of its MetaL episode. TFRS first pulls each support embedding toward its nearest base-class prototypes, and then pushes the whole set away from their shared centroid, so the episode begins its similarity search with clips that already sit closer to class-specific motion patterns and farther from task-agnostic background cues (Figure 10). In the AMeFu+TFRS and STRM+TFRS variations, the same episodic refinement has to repair 2D ResNet-50 embeddings that were learned from static ImageNet images. Thus, the advantage across the different model variations is not a different outer-loop algorithm but the synergy between TFRS's episode-level bias correction and a backbone whose pre-training already matches the temporal nature of video actions. FedSLAR++ also reaches the top of Table 4 likely because its transformer already encodes long-range motion cues, so the inner-loop prototype update only has to tip those features toward the novel class centers, pushing the UCF101 accuracy results, using ProtoNet (Figure 11). The rest of the works on that dataset aim at the same problem; however, they start from different priors, so their meta-updates have to learn motion and not just simply refine it.

The patterns for the rest of the public datasets follow similar patterns in terms of the works that reach the top three. For the HMDB51 dataset (Table 5), CLIP-MDMF [162], along with the FedSLAR++ [163] reach the top of the list, being two pipelines that start with broad appearance priors and then use meta-updates in order to refine them. Regarding the SSv2-full dataset (Figure 6), it defines actions through motion direction and object change, so methods that force the encoder to align frames during the meta-training stage seem to work best. The Chamfer+TFRS [175] and the FedSLAR++ [163] are still top of the list, with HyRSM [141] following in third place. A relation network learns the existing motion relationships through the analysis of every single frame in the support clip, as well as its connection with every query frame. It creates a relation embedding and then measures their similarity through a bidirectional Hausdorff set distance. The relation embeddings along with the similarity metric are re-learned inside every MetaL episode, allowing the network to adapt from a single example to new action classes.

Finally, results on the Kinetics-100 dataset (Figure 7) show a top result at 96.5% for 1-shot and at 98.5% for the 5-shot setup with the Chamfer+TFRS model [175], while the results of the rest of the works drop down by more than 20%. In this work, the Sports-1M pretraining, along with the 3D R(2+1)D-34 backbone, provides strong
<figure><img src="image_12.png" /><figcaption>FIGURE 10. A schematic representation of the proposed TFRS approach for few-shot human action recognition from [175].</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>FIGURE 11. The MetaL process using ProtoNet from [163].</figcaption></figure>

motion-aware features, and then a Chamfer loss finds the best match between support and query frames. The aligned frames go through a transformer relation head, and the whole path is trained end-to-end, so each one-shot episode starts with features already lined up in time, reaching the aforementioned results. The rest of the methods in Table 7, however, do not include this alignment. MetaUVFS [137] learns good features without labels but still averages whole clips, stalling much lower than the top performance of Chamfer+TFRS. References [143] and [148] re-weight or filter frames yet leave their order untouched, remaining in a lower performance range. References [160] and [176] refine their class prototypes so they represent each action more cleanly, but they still form those prototypes by averaging

all the frames in a clip before making any comparison. By blending frame-level features before matching, both methods lose fine temporal details, and their accuracy also drops, compared to the Chamfer+TFRS one.

## VII. DISCUSSION AND FUTURE INSIGHTS

Over the years, MultiTL and MetaL approaches have provided important contributions to the research on human movement representation and recognition. MultiTL improves data efficiency by sharing information among different tasks through shared parameters. MetaL, on the other hand, adapts those shared parameters when new, unseen data are encountered, reducing the needed task-specific fine-tuning. However, neither approach has managed to eliminate the need
<table><caption>TABLE 4. 1-shot and 5-shot meta learning accuracy results on the UCF101 dataset.</caption><thead><tr><th>Method</th><th>Year</th><th>Pre-training</th><th>Backbone</th><th>1-shot %</th><th>5-shot %</th></tr></thead><tbody><tr><td>MetaUVFS [137]</td><td>2021</td><td>No</td><td>ResNet-50</td><td>76.3</td><td>92.5</td></tr><tr><td>HyRSM [141]</td><td>2022</td><td>ImageNet</td><td>ResNet-50</td><td>83.9</td><td>94.7</td></tr><tr><td>He et al. (CossAttn) [136]</td><td>2022</td><td>No</td><td>R(2+1)D-18</td><td>-</td><td>97.0</td></tr><tr><td>He et al. (Inner Product) [136]</td><td>2022</td><td>No</td><td>R(2+1)D-18</td><td>-</td><td>96.8</td></tr><tr><td>RFPL+OTAM [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>84.3</td><td>92.1</td></tr><tr><td>RFPL+TRX [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>82.5</td><td>96.3</td></tr><tr><td>RFPL + STRM [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>79.7</td><td>96.7</td></tr><tr><td>TADRNet [143]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>86.7</td><td>96.4</td></tr><tr><td>FTAN [145]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>85.2</td><td>95.5</td></tr><tr><td>HCR [177]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>71.8</td><td>87.3</td></tr><tr><td>HCR [177]</td><td>2024</td><td>IG-65M</td><td>R(2+1)D</td><td>88.9</td><td>95.7</td></tr><tr><td>TgGTM [161]</td><td>2024</td><td>CLIP</td><td>CLIP-RN50</td><td>91.0</td><td>96.0</td></tr><tr><td>TgGTM [161]</td><td>2024</td><td>CLIP-ViT-B</td><td>ResNet-50</td><td>96.2</td><td>99</td></tr><tr><td>TSA-MLT [158]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>80.6</td><td>97.1</td></tr><tr><td>DPN [160]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>87.7</td><td>98.0</td></tr><tr><td>CLIP-MDMF [162]</td><td>2024</td><td>CLIP</td><td>CLIP-RN50</td><td>94.3</td><td>98.8</td></tr><tr><td>CLIP-MDMF [162]</td><td>2024</td><td>CLIP</td><td>CLIP-ViT-B</td><td><strong>97.0</strong></td><td><strong>99.3</strong></td></tr><tr><td>MRLN [7]</td><td>2024</td><td>No</td><td>ResNet-50</td><td>86.9</td><td>96.3</td></tr><tr><td>HyRSM++ [157]</td><td>2024</td><td>ImageNet</td><td>2D ResNet-50</td><td>85.8</td><td>95.9</td></tr><tr><td>Chamfer+TFRS [175]</td><td>2024</td><td>Sports-1M</td><td>3-D R(2 + 1)D-34</td><td><strong>97.8</strong></td><td><strong>99.5</strong></td></tr><tr><td>STRM+TFRS [175]</td><td>2024</td><td>ImageNet</td><td>2-D ResNet-50</td><td>83.7</td><td>97.2</td></tr><tr><td>AMeFu+TFRS [175]</td><td>2024</td><td>ImageNet</td><td>2-D ResNet-50</td><td>86.4</td><td>95.7</td></tr><tr><td>MGTSN [178]</td><td>2024</td><td>ResNet-50</td><td>ResNet-50</td><td>-</td><td>97.6</td></tr><tr><td>CCLN [176]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>86.9</td><td>96.1</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>65.9</td><td>81.3</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>MViT</td><td>51.9</td><td>72.5</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>STCA</td><td>59.8</td><td>78.3</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>68.1</td><td>80.8</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>MViT</td><td>48.2</td><td>68.1</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>STCA</td><td>63.0</td><td>79.1</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400/IG-65M</td><td>R(2+1)D</td><td>82.6</td><td>94.5</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400</td><td>MViT</td><td><strong>97.2</strong></td><td><strong>99.4</strong></td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400</td><td>STCA</td><td>92.1</td><td>97.5</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400/IG-65M</td><td>R(2+1)D</td><td>83.3</td><td>95.0</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400</td><td>MViT</td><td><strong>97.3</strong></td><td><strong>99.6</strong></td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400</td><td>STCA</td><td>93.3</td><td>97.8</td></tr></tbody></table>

for the intensive data-collection processes that characterize the ML stage.

## A. FOUNDATION MODELS VERSUS TASK-STRUCTURED APPROACHES

Recent advances in generative AI and foundation models are viewed as a potential solution to the challenge of data scarcity. Such models undergo unsupervised pretraining on massive amounts of data, yielding impressive results, reducing dependence on fully annotated datasets, and enabling few-shot learning, in the case where the target distribution is well-covered by pretraining. The question that arises is whether this renders task-structured approaches redundant, with the answer being negative in the case of human movement recognition. Fine-grained gestures are tied to objects, tools, occlusions, and collaboration patterns among humans, which are generally not covered by large-scale models. Challenges associated with biometric data privacy and confidentiality further limit the data collected and deployed. Thus, foundation models can improve the starting point for human movement recognition; however,

they cannot be the only solution, as they do not remove the need for explicit structure and rapid, curated adaptation in movement-centric applications.

## B. LEVERAGING TASK STRUCTURE FOR EFFICIENT GENERALIZATION

While synthetic data can indeed offer a partial solution to the problem of data scarcity, a more fundamental solution may be connected to the way tasks, and therefore human movements, are structured. Quite a few MultiTL approaches still define tasks by modality or sensor type, without considering the inherent hierarchy and stochasticity of human movement. A few works have attempted to deploy such structures, with [86] specifically proposing a hierarchical benchmark for gesture recognition, dividing movement into event-sets-elements and assigning each level to a separate MultiTL path. Similarly, [64] explores sequence- and clip-level classification as co-evolving tasks, allowing local cues to regularize broader temporal understanding. These efforts demonstrate that task decomposition is not merely architectural, but a methodological decision that
<table><caption>TABLE 5. 1-shot and 5-shot meta learning accuracy results on the HMDB51 dataset.</caption><thead><tr><th>Method</th><th>Year</th><th>Pre-training</th><th>Backbone</th><th>1-shot</th><th>5-shot</th></tr></thead><tbody><tr><td>Cao et al. [139]</td><td>2021</td><td>K400</td><td>MFNet-C50</td><td>63.4</td><td>79.6</td></tr><tr><td>MetaUVFS [137]</td><td>2021</td><td>No</td><td>No</td><td>47.5</td><td>66.1</td></tr><tr><td>HyRSM [141]</td><td>2022</td><td>ImageNet</td><td>ResNet-50</td><td>60.3</td><td>76.0</td></tr><tr><td>He et al. (CosAttn) [136]</td><td>2022</td><td>No</td><td>R(2+1)D-18</td><td>-</td><td>81.8</td></tr><tr><td>He et al. (Inner product) [136]</td><td>2022</td><td>No</td><td>R(2+1)D-18</td><td>-</td><td>82.4</td></tr><tr><td>TADRNet [143]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>64.3</td><td>78.2</td></tr><tr><td>CLIP-MDMF [162]</td><td>2024</td><td>CLIP</td><td>CLIP-RN50</td><td>66.8</td><td>83.0</td></tr><tr><td>CLIP-MDMF [162]</td><td>2024</td><td>CLIP</td><td>CLIP-VIT-B</td><td>77.1</td><td>88.0</td></tr><tr><td>TgGTM [161]</td><td>2024</td><td>CLIP-VIT-B</td><td>CLIP-RN50</td><td>80.3</td><td>89.2</td></tr><tr><td>HyRSM++ [157]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>61.5</td><td>76.4</td></tr><tr><td>MGTSN [178]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>-</td><td>79.4</td></tr><tr><td>CCLN [176]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>65.1</td><td>78.8</td></tr><tr><td>HCR [177]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>48.6</td><td>63.8</td></tr><tr><td>HCR [177]</td><td>2024</td><td>IG-65M</td><td>R(2+1)D</td><td>67.5</td><td>79.3</td></tr><tr><td>MRLN [7]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>65.5</td><td>78.6</td></tr><tr><td>TSA-MLT [158]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>57.9</td><td>78.2</td></tr><tr><td>FTAN [145]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>62.2</td><td>76.6</td></tr><tr><td>DMSD [172]</td><td>2025</td><td>No</td><td>ResNet-18</td><td>39.6</td><td>54.9</td></tr><tr><td>DPN [160]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>60.2</td><td>76.6</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>STCA</td><td>42.2</td><td>57.4</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>STCA</td><td>42.9</td><td>58.5</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400/IG-65M</td><td>R(2+1)D</td><td>54.1</td><td>73.1</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400/IG-65M</td><td>R(2+1)D</td><td>54.9</td><td>73.7</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400</td><td>MViT</td><td>73.6</td><td>87.8</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400</td><td>MViT</td><td>73.0</td><td>87.4</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400</td><td>STCA</td><td>62.8</td><td>81.4</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400</td><td>STCA</td><td>63.5</td><td>80.0</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>MViT</td><td>35.4</td><td>42.4</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>MViT</td><td>38.0</td><td>49.4</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>43.2</td><td>54.6</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>43.9</td><td>54.7</td></tr></tbody></table>

directly affects generalization and robustness, particularly when data is sparse or temporally fragmented. In most works, though, there is no true semantic relation to the main task in the MultiTL setup. In response to this, and in an attempt to handle the complexity of human movement as presented in the Introduction, the authors of this survey proposed a hierarchical MultiTL methodology [179], which decomposes human movement in professional environments into movement primitives, actions, and activities. This structure not only enables the decomposition of movement into simpler, manageable components but also preserves the semantic connection among the tasks in the MultiTL setup, seeking to address task-relatedness through domain knowledge, rather than empirical tuning. This approach aims to not only improve recognition accuracy but to enable more interpretable and generalizable models, as structuring tasks according to the logic of the domain actually enables MultiTL to exchange knowledge across related objectives, while preserving the characteristics of human behavior.

### C. FROM METRICS TO STRUCTURES IN TASK-RELATEDNESS

As mentioned already, one of the main challenges in MultiTL, not only for human action recognition, but broadly, as shown from the rest of the existing surveys, is

task-relatedness itself. The success of MultiTL models often depends on the way tasks are grouped and knowledge among them is shared. As shown in the above analysis, most works in the field of human action recognition address this relatedness empirically, without the use of any metrics or strategies guiding task selection. In other fields, such as NLP, methods have been proposed to model inter-task similarities and feature overlapping. However, in human movement recognition, this formalism is absent, probably due to the complexity of motion data and the diversity of the experimental setups. This may also be connected to the context-specific nature of task-relatedness itself. Two or more tasks can be related in a specific industrial environment, but unrelated in another, thus relatedness can vary depending on the goal and context. Such an observation leads to a broader question of whether the community needs universal metrics of task-relatedness or if task-relatedness is inherently scenario-dependent. On the one hand, the lack of metrics makes it difficult to compare the challenging nature of tasks in MultiTL use-cases; however, on the other hand, enforcing rigid relationship definitions may lead to missing the domain-specific logic that characterizes real-world applications. After all, relatedness might not be connected only to the shared features or similar distributions, but also to the functional role of movement, which could be
<table><caption>TABLE 6. 1-shot and 5-shot meta learning accuracy results on the SSv2 dataset.</caption><thead><tr><th>Method</th><th>Year</th><th>Pre-training</th><th>Backbone</th><th>1-shot</th><th>5-shot</th></tr></thead><tbody><tr><td>HyRSM [141]</td><td>2022</td><td>ImageNet</td><td>ResNet-50</td><td><strong>54.3</strong></td><td><strong>69.0</strong></td></tr><tr><td>RFPL+OTAM [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>47.0</td><td>61.0</td></tr><tr><td>RFPL+TRX [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>44.6</td><td>64.6</td></tr><tr><td>RFPL+STRM [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>45.7</td><td>69.5</td></tr><tr><td>TADRNet [143]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>43.0</td><td>61.1</td></tr><tr><td>Chamfer+TFRS [175]</td><td>2024</td><td>Sports-1M</td><td>3-D R(2 + 1)D-34</td><td><strong>68.7</strong></td><td><strong>81.8</strong></td></tr><tr><td>CLIP-MDMF [162]</td><td>2024</td><td>CLIP</td><td>CLIP-RN50</td><td>56.9</td><td>68.0</td></tr><tr><td>CLIP-MDMF [162]</td><td>2024</td><td>CLIP</td><td>CLIP-VIT-B</td><td>60.1</td><td>72.7</td></tr><tr><td>HyRSM++ [157]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>55.0</td><td>69.8</td></tr><tr><td>MGTSN [178]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>-</td><td>65.9</td></tr><tr><td>CCLN [176]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>46.0</td><td>61.3</td></tr><tr><td>MRLN [7]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>45.9</td><td>61.3</td></tr><tr><td>TSA-MLT [158]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>43.8</td><td>65.9</td></tr><tr><td>DPN [160]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>46.8</td><td>64.7</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>23.5</td><td>25.4</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>32.4</td><td>42.7</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>MViT</td><td>23.4</td><td>29.1</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>MViT</td><td>34.3</td><td>43.0</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>STCA</td><td>24.4</td><td>28.1</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>STCA</td><td>28.6</td><td>36.2</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400/IG-65M</td><td>R(2+1)D</td><td>45.0</td><td>59.0</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400/IG-65M</td><td>R(2+1)D</td><td>45.9</td><td>59.7</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400</td><td>MViT</td><td>54.5</td><td>70.0</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400</td><td>MViT</td><td><strong>64.3</strong></td><td><strong>79.3</strong></td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400</td><td>STCA</td><td>47.2</td><td>64.6</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400</td><td>STCA</td><td>58.0</td><td>72.8</td></tr><tr><td>DMSD [172]</td><td>2025</td><td>No</td><td>ResNet-18</td><td>26.7</td><td>32.1</td></tr></tbody></table>

<table><caption>TABLE 7. 1-shot and 5-shot meta learning accuracy results on the Kinetics-100 dataset.</caption><thead><tr><th>Method</th><th>Year</th><th>Pre-training</th><th>Backbone</th><th>1-shot</th><th>5-shot</th></tr></thead><tbody><tr><td>MetaUVFS [137]</td><td>2021</td><td>No</td><td>No</td><td>62.8</td><td>79.5</td></tr><tr><td>HyRSM [141]</td><td>2022</td><td>ImageNet</td><td>ResNet-50</td><td>73.7</td><td>86.1</td></tr><tr><td>TADRNet [143]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>75.6</td><td>87.4</td></tr><tr><td>RFPL+OTAM [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>74.6</td><td>86.8</td></tr><tr><td>RFPL+TRX [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>66.2</td><td>87.3</td></tr><tr><td>RFPL+STRM [148]</td><td>2023</td><td>ImageNet</td><td>ResNet-50</td><td>64.9</td><td>87.7</td></tr><tr><td>CCLN [176]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>75.8</td><td>87.5</td></tr><tr><td>MRLN [7]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>75.7</td><td>87.6</td></tr><tr><td>FTAN [145]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>73.4</td><td>86.6</td></tr><tr><td>Chamfer+TFRS [175]</td><td>2024</td><td>Sports-1M</td><td>3-D R(2 + 1)D-34</td><td><strong>96.5</strong></td><td><strong>98.5</strong></td></tr><tr><td>DPN [160]</td><td>2024</td><td>ImageNet</td><td>ResNet-50</td><td>76.3</td><td>90.7</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>43.5</td><td>57.7</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>R(2+1)D</td><td>39.2</td><td>53.4</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>MViT</td><td>42.1</td><td>58.2</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>MViT</td><td>36.4</td><td>52.1</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>No</td><td>STCA</td><td>49.1</td><td>59.1</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>No</td><td>STCA</td><td>43.5</td><td>55.9</td></tr><tr><td>FedFSLAR++ CD [163]</td><td>2024</td><td>K400/IG-65M</td><td>R(2+1)D</td><td>69.7</td><td>84.6</td></tr><tr><td>FedFSLAR++ FD [163]</td><td>2024</td><td>K400/IG-65M</td><td>R(2+1)D</td><td>78.9</td><td>90.2</td></tr></tbody></table>

lost in specific metrics. Our survey suggests that a promising middle ground lies in structuring tasks through domain-connected structures, rather than seeking a one-size-fits-all relatedness metric.

## D. TOWARD REAL-WORLD GENERALIZATION

Another topic that emerged through this survey was the reliance on public benchmarks that have been presented in this survey and are generally deployed for evaluation

purposes by the whole ML community. Datasets like UCF101, HMDB51, Kinetics, NTU RGB+D, and SSv2 provide a solution for comparing different methodologies. However, a valid question is whether the presented works in MultiTL and MetaL can really reveal whether the performance gains in one dataset can be translated as the generalization capability of a model to other domains, uses, or environmental conditions. A model that performs well across splits of a dataset may fail when faced
with unseen body types, new objects, or gestures. These kinds of challenges particularly concern human movement in professional environments, where human movement is explicitly connected to work routines, ergonomic constraints, and intra-person variabilities, conditions that are not always reflected in the public benchmarks. Furthermore, in scenarios such as that of human-robot collaboration, actions are not always distinctive units, but procedures, habits, and roles, and recognizing these requires a model able to adapt and generalize. This shows the need for new, well-curated datasets that will reflect real-world scenarios, with realistic noise, complex temporal dependencies, intra-person, as well as inter-person variations. Adding to this, it is also important to shift from the accuracy as the mere metric of evaluation, to other metrics that could also measure the adaptability and generalization capabilities of a model when unseen data appear. MultiTL and MetaL can meaningfully contribute here, but not only when tested under conditions that challenge their assumptions and expose their limitations.

## E. MULTI-TASK LEARNING AND META LEARNING CONNECTIONS AND HYBRID NEEDS

Throughout the survey, MultiTL and MetaL were treated as learning paradigms that can handle the recognition of human movement in two different ways, having different objectives and goals. The goal of MultiTL is to improve the performance of specific tasks through the exchange of knowledge, while that of MetaL is to generalize and adapt to unseen, new data through minimal fine-tuning, optimization, and initialization. However, through the survey, it was observed that the main challenge for MultiTL is task-relatedness, while for MetaL, issues of task distribution also arise. Aligned with the definition of task-relatedness, the success of few-shot adaptation often depends on how semantically aligned the used data is. In both cases, models seem to benefit from the underlying notion of relatedness, consistent distributions, showing that reliance on implicit assumptions on the used tasks exists in both methods. This invites a shift towards new models that, instead of treating MultiTL and MetaL as separate toolkits, or even hybrid ones that only complement each other, will rather embed their principles from the very beginning. Thus, these approaches should be thought of as puzzle-like pieces that come to fill in the gaps, but as new frameworks where task synergies, generalization, and adaptability will be entangled in the model's architecture and learning process. Such models would be able to learn how to share and adapt simultaneously, guided by the structure of movement data and their temporal, spatial, and contextual semantics. However, the development of such models requires a deep understanding of how movement dynamics interact, and the fact that such a model needs to encapsulate the complexity layers of human movement.

## VIII. KEY TAKEAWAYS AND CONCLUSION

This survey presented a non-exhaustive cartography of the evolution of the methods of MultiTL and MetaL over the past

ten years. In MultiTL, a progression towards auxiliary heads, gating units, and uncertainty-based weighting was observed; however, the challenge of negative transfer persisted, and that of task-relatedness remained unhandled. MetaL, on the other hand, has benefited from new tools and architectures for rapid adaptation and generalization over the years, though it still struggles with domain shifts and propagation of biases inherited from the base learner. When placed side by side, these two approaches seem to have some fundamental common needs and objectives that could potentially lead to new expressions of them, able to exchange knowledge while adapting efficiently to new, unseen data.

Where current practice still struggles is not in the idea of sharing or adapting per se, but in how we decide what to share, how we couple the tasks in the loss, and how the complexity of human movement is respected. In most setups, task-relatedness is assumed rather than estimated, the total loss is a fixed sum with fixed weights, and temporal structure is treated as an afterthought. This observation further highlights the need for future works to focus on developing dynamic loss weighting mechanisms, able to adapt to new, unseen data, rather than relying on fixed parameters. Similarly, explicitly modeling task-relatedness through hierarchical domain structures, or established metrics, could offer a principled alternative to the empirical task grouping observed in current works.

This motivates a question central to the authors' future work: Does a single model that both shares knowledge across tasks and adapts rapidly through one unified objective outperform sequential or loosely coupled hybrids when tested on unseen conditions? By unified objective, it is meant that sharing and adaptation are optimized together rather than in separate stages. If that coupling is learned, and not manually set, it should be able to reduce conflict among data, smooth predictions over time, as well as the number of needed labels and gradient steps for proper adaptation and generalization. Such unified or hybrid frameworks, where MultiTL and MetaL principles are embedded jointly from the beginning, represent a promising direction for future research, offering a pathway toward models that share, weight, and adapt in a coherent and data-aware manner.

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<figure><img src="image_14.png" /></figure>

**GAVRIELA SENTERI** received the B.S. degree in applied informatics from the University of Macedonia, in 2016, and the M.S. degree in computational intelligence and digital media from the Aristotle University of Thessaloniki, in 2019. She is currently pursuing the Ph.D. degree in gesture recognition for professional environments with Mines Paris-Université PSL. Her research is focused on vision and sound modules for the human movement analysis and recognition of expert professionals in the handicraft and manufacturing industries.

<figure><img src="image_15.png" /></figure>

**SOTIRIS MANITSARIS** received the B.S. degree in applied mathematics from the Aristotle University of Thessaloniki, the dual master's degree in local development from the University of Blaise Pascal and the Engineering School, University of Thessaly, and the Ph.D. degree in AI from the University of Macedonia. He is currently the Deputy Director with the Centre for Robotics, Mines Paris-Université PSL. His research interests focus on human-centered AI that consists of applying

machine learning and pattern recognition concepts and methods to signals recorded from the human body and used as modalities for collaborating with intelligent machines.

<figure><img src="image_16.png" /></figure>

**ALINA GLUSHKOVA** received the B.S. degree in information and communication from the University of Paris 8, the dual master's degree in management from the Paris-Panthéon-Assas University and Paris Dauphine University, in 2012, and the Ph.D. degree in applied informatics from the University of Macedonia, in 2016. Her research focuses on human-computer interaction and developing systems and algorithms that improve machines' perception and make them

capable of analyzing and evaluating human movement to provide feedback that would guide the gestural/postural performance of the learner.