# Re-examining Concept-based Explainable Models for Multimodal Interpretative Tasks

Julie Tores, Elisa Ancarani, Rémy Sun, Lucile Sassatelli, Hui-Yin Wu,
Frederic Precioso

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Julie Tores, Elisa Ancarani, Rémy Sun, Lucile Sassatelli, Hui-Yin Wu, et al.. Re-examining Concept-based Explainable Models for Multimodal Interpretative Tasks. MM 2025 - 33rd ACM International Conference on Multimedia, Oct 2025, Dublin, Ireland. pp.12437-12445, 10.1145/3746027.3758170. hal-05462454

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RESEARCH-ARTICLE

Re-examining Concept-based Explainable Models for Multimodal
Interpretative Tasks

JULIE TORES, University of Côte d'Azur, Nice, Provence-Alpes-Cote d'Azur, France

ELISA ANCARANI, University of Côte d'Azur, Nice, Provence-Alpes-Cote d'Azur, France

RÉMY SUN, University of Côte d'Azur, Nice, Provence-Alpes-Cote d'Azur, France

LUCILE SASSATELLI, University of Côte d'Azur, Nice, Provence-Alpes-Cote d'Azur, France

HUIYIN WU, University of Côte d'Azur, Nice, Provence-Alpes-Cote d'Azur, France

FRÉDÉRIC PRÉCIOSO, University of Côte d'Azur, Nice, Provence-Alpes-Cote d'Azur, France

Open Access Support provided by:

University of Côte d'Azur

<figure><img src="image_3.png" /></figure>
# Re-examining Concept-based Explainable Models for Multimodal Interpretative Tasks

Julie Tores

Université Côte d'Azur, CNRS, Inria,
I3S

Sophia Antipolis, France
julie.tores@univ-cotedazur.fr

Elisa Ancarani

Université Côte d'Azur, CNRS, I3S
Sophia Antipolis, France

elisa.ancarani@univ-cotedazur.fr

Rémy Sun

Université Côte d'Azur, Inria, CNRS,
I3S

Sophia Antipolis, France
remy.sun@inria.fr

Lucile Sassatelli

Université Côte d'Azur, CNRS, I3S
Sophia Antipolis, France

lucile.sassatelli@univ-cotedazur.fr

Hui-Yin Wu

Université Côte d'Azur, Inria
Sophia Antipolis, France

hui-yin.wu@inria.fr

Frederic Precioso

Université Côte d'Azur, CNRS, Inria,
I3S

Sophia Antipolis, France
precioso@i3s.unice.fr

## Abstract

Concept-based models have been proposed as a new line of research for explainable by-design deep learning models. However, those models show their whole power when applied to benchmarks where the concepts are well defined and the concepts' attributes easily extractable from the raw data. In this paper, we challenge the most recent concept-based model initially developed for image classification, on more complex interpretative tasks from a recently proposed video benchmark where they perform poorly. We conduct a root cause analysis of the poor performances of state-of-the-art explainable concept-based models for these multimodal interpretative tasks, and propose adaptations to design robust explainable models for detecting character objectification in this novel challenging video benchmark. We show that the optimal architectural choice may vary depending on the modality setting, thereby showing that designing multimodal concept-based approaches remains an open challenge and calls for further investigation.

## CCS Concepts

* **Computing methodologies** → **Neural networks**; *Scene understanding*; *Social and professional topics* → User characteristics.

## Keywords

Video interpretation; Multimodality; Explainable AI; Concept annotation

## ACM Reference Format:

Julie Tores, Elisa Ancarani, Rémy Sun, Lucile Sassatelli, Hui-Yin Wu, and Frederic Precioso. 2025. Re-examining Concept-based Explainable Models for Multimodal Interpretative Tasks. In *Proceedings of the 33rd ACM International Conference on Multimedia (MM '25), October 27–31, 2025, Dublin,*

Ireland. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3746027.3758170

# 1 Introduction

As deep learning tackles ever more complex and critical tasks, adding information to further describe the input provides a clear and actionable path towards improvement [19, 25]. While the gold standard computer vision benchmarks ImageNet [5] and MS-COCO [14] choose not to use additional information, in-depth analyses of both the annotating processes and the resulting annotations for ImageNet and MS-COCO, have raised strong concerns [3, 6] showing that even apparently universal concepts can lead to harmful social biases, in turn biasing models. To tackle more complex image interpretation tasks, recent benchmarks have integrated domain expert knowledge, for instance for fine-grained medical image interpretation with benchmarks such as *SkinCon* [4] or *MedTrinity-25M* [26].

Character objectification detection [23] is an emerging highly complex video interpretation task. Character objectification seeks to detect within movie clips, whether characters are objectified, meaning that they are portrayed as objects of desire or objects of service rather than subjects of action. Understanding such high-level constructs is crucial for analyzing the representations of social dynamics in audiovisual contents, making this task central for researchers in media studies. Tores et al. [23] introduced a dedicated dataset for this novel task, ObyGaze, which was further extended to a multimodal version MObyGaze [22].

Given the complex nature of the task, it is crucial that deep learning solutions be understandable and verifiable by social scientists. In order to support explainability and facilitate interdisciplinary collaboration, MObyGaze provides additional information on movie clips beyond the yes or no label for objectification: each movie clip is annotated for the presence of 11 possibly objectifying elements (such as type of shot, posture, speech, type of voice) spanning three modalities (vision, text, audio). These *objectifying factors* are in fact in line with the notion of *concepts* in the field of explainable AI. As such, this new fine grained annotation could provide a unique bridge to help understand the model's decisions.

Under the light of this new video interpretative task, we wish to make use of state of the explainable models on MObyGaze, but

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https://doi.org/10.1145/3746027.3758170
these models prove very difficult to use on this complex dataset. **Our Brave New Idea is to conduct a root cause analysis of the poor performances of state-of-the-art explainable concept-based models for multimodal interpretative tasks, in order to design robust explainable models for detecting character objectification in movies.** We show that building explainable models for this kind of task requires revisiting some of the assumptions commonly made when working with usually larger datasets, and with less interpretative concepts.

Generally, rich annotations of multimodal data with labels on the objectification level and on the associated multimodal factors, present the following challenges:

*   **C1 Complex factors:** Several factors (or concepts) contribute to the final objectification level, and each of these factors is itself interpretative (e.g., for visual modality, identifying objectification through the concept *Appearance* because there is a situational discrepancy such as clothes too revealing while the scene is outside in the snow, or for audio modality identifying objectification through the concept *Voice* because there is a situational discrepancy between an infantile voice and the sensuality of the scene). These interpretative complex factors are thus both challenging to annotate and challenging to detect with Machine Learning models.

*   **C2 Weak supervision:** Video annotation is tedious and the annotators delimit the scenes of interest to annotate the high-level construct of objectification. However the factors and perception of objectification may not span the entire segment and are often localized in time within the delimited sequence. Furthermore, interpretative annotations demand for domain-specific insights, which significantly increases cost and limits the size of available datasets. This scarcity of high-quality annotated data necessitates to train models carefully, taking the most of the supervision.

*   **C3 Fusion with modality unbalance:** The perception of objectification is produced by a combination of concepts from different modalities [22]: filmic concepts (Type of shot, Look), inconographic concepts (Appearance, Body...), textual concept (Speech transcript) and audio concepts (Voice, Soundtrack). Not only the presence of the objectification construct may span a small fraction of duration of the movie, but the concepts may appear with strongly unbalanced frequencies (e.g., in MObyGaze, audio is the least represented concept modality, after vision and text). This variability makes the objectification detection task more complex, as models must learn to interpret nuanced patterns across modalities.

*   **C4 Explainability:** It is essential to have explainable models where the patterns behind decisions remain accessible and auditable to experts in social sciences. It is crucial that these decisions and these patterns can be expressed or at least represented in the space of concepts which were provided by these experts as basis of the objectification construct. Explainable models where the concepts are explicitly involved

allow experts in social sciences to probe the internal mechanisms of the decision and to provide expert feedback.

In this work, we investigate **Challenge C4: how to design concept-based models for multimodal video data and not only image data**, to provide explanations useful to experts in media studies. We specifically show that Concept Embedding Models (CEM) [28], a current state-of-the art concept-based architecture, is challenged by such an interpretative task. CEM have demonstrated in the last years very interesting trade-offs between decision accuracy and explainability potential [16, 28], when evaluated on carefully handcrafted image datasets like CelebA [15] and CUB-200-2011 (CUB) [25] with mostly non-interpretative concepts [2, 12, 16]. Although these benchmark datasets are well-suited for evaluating concept-based model performance, they often lack the complexity of real-world media content such as multimodality, video, interpretative constructs (actually all of these at once in the context of this work).

**Our contributions are:**

*   We show that CEM struggles on multimodal interpretative task where concepts span multiple modalities and are informative for the downstream task (i.e. objectification detection, also referred to as the end task), even though difficult to detect. We show that CEM results are significantly worse on MObyGaze than on CUB and on CelebA image datasets, which have been originally used to benchmark CEM and to demonstrate its power. We carry out a root-cause analysis to identify the key dimensions where MObyGaze differs from CUB and CelebA.

*   We thereby show that by re-examining CEM under the light of the MObyGaze dataset, some key assumptions no longer hold: the CEM end-to-end training strategy prevents effective concept detection (in our settings of smaller dataset and scarser concept presence); the concept representations are not sufficient to achieve good results on the downstream task when the concepts do not fully determine this end task. We specifically propose CPT, CPT-F and CPT-FD to make CEM resilient to these two assumptions. Experimental evaluation confirms both our design hypotheses and the usefulness of our proposed adaptations, with increase in interpretability and task accuracy of up to 10%.

*   We hence show that CEM end-to-end-learning is a weakness that can be alleviated, but that **depending on the modalities considered, possible solutions differ, thereby showing that multimodal explainability with prominent concept-based approaches like CEM remain an open problem to investigate.**

The article is organized as follows. Sec. 2 positions our contributions in the context of related works. Sec. 3 presents background on CEM, assesses CEM performance on MObyGaze and introduces options to alleviate the weaknesses of legacy CEM. Sec. 4 presents the experimental setup and results, and Sec. 5 concludes the paper.
## 2 Related Works

We contextualize our work in relation to explainable concept-based approaches and existing datasets that have been annotated with concepts.

### 2.1 Concept based approaches for XAI

To improve the transparency of deep learning models, the community has been designing Explainable AI (XAI) methods [1, 10, 21, 29] aimed at clarifying the decision processes behind model predictions.

A subset of XAI methods includes concept-based approaches [9, 17, 27], such as Concept Bottleneck Models (CBMs) [13]. A CBM first estimates from the input data the presence or absence of pre-defined concepts annotated in the dataset. This vector of presence probabilities is then fed to an interpretable classifier (e.g., a decision tree) for the final downstream task. A notable advantage of CBMs is their support for interventions, enabling experts to enhance task performance by correcting or arbitrarily setting concepts during training or inference. This capability is particularly crucial in video-interpretation tasks, where the concept detection is hard. A major weakness of CBMs is however that the final classifier does not have access to input information beyond the rating of concepts presence. That is why Concept Embedding Models (CEMs) have been introduced [28]. They instead project the input data into a higher-dimensional latent space to enable the preservation of the information from the original input data, while still allowing interventions on concepts. CEMs therefore appear as a promising method to design explainable models for interpretative tasks.

### 2.2 Datasets annotated with concepts

CEM has been evaluated on highly curated and carefully hand-crafted image datasets like the CUB-200-2011 (Caltech-UCSD Birds) [25] and CelebA (CelebFaces Attributes Dataset) [15].

The CUB-200-2011 (CUB) dataset contains images of 200 bird species annotated with 312 human-interpretable concepts like "wing color" or "size". Since CUB annotation was done by non-experts, annotations were noisy (e.g., "black wings" vs. "dark grey wings"), leading to some concept ambiguity. To ensure that all birds of the same species share the same set of concepts, annotations have been denoised and concepts with very few occurrences were filtered, leaving 112 final concepts, resulting in a very clean and curated CUB dataset.

The CelebA dataset contains images of celebrity faces. Each image is annotated with 40 attributes like eyeglasses, wearing hat. Even so highly curated, the CelebA dataset includes concepts that introduce a certain level of subjectivity, such as "attractive", "chubby", or "narrow eyes". Besides raising ethical questions, as highlighted in [20], the subjective nature of these concepts make detecting them more challenging.

Video datasets annotated with concepts are rare. Recently, a new movie dataset named ObyGaze, has been introduced in [23], and extended to a multimodal version MObyGaze in [22], for the purpose of a novel video-interpretation task: how character objectification in films is produced through subtle complex temporal patterns. To minimize the impact of subjectivity in the annotation process, a detailed thesaurus defining visual objectification with coarse-grained concepts and examples has been designed by a team

involving media studies experts. The dataset is annotated with 11 coarse-grained concepts (Type of shot, Look, Body, Posture, Clothing, Appearance, Expression of emotion, Activity, Speech, Voice and Soundtrack), and exemplified with instances like "voyeuristic man gaze on woman" for the Look concept. There are 3 objectification labels (i.e. the end classification task): Easy Negative (EN) where objectification is labeled as absent and no elements suggestive of objectification is annotated, Hard Negative (HN) where objectification is labeled as absent but presence of objectifying concepts is annotated (yet they are not sufficient to perceive a character as objectified) and Sure (S) where objectifying concepts are annotated present and sufficient to result in the perception of a character as an object.

Although concept-based approach like CEM are well studied for images, their evaluation on challenging realistic video interpretation tasks has yet to be explored, our work aims to address this gap.

## 3 Method

This section introduces the theoretical background of CEMs (Sec. 3.1), re-examines CEM performance on CUB, CelebA and MObyGaze (Sec. 3.2), and presents options to improve the performance of CEM-type models for multimodal video-interpretation datasets (Sec. 3.3).

### 3.1 Background on CEM

**Model:** CEM, that we adapt for video and depict in Fig. 2.a, first processes the chosen input representation (from a pre-trained model) into a core feature representation *h*, which is fed to each concept detector module named MLP*, resulting into positive (presence) embedding $\hat{c}_i^+$ and a negative (absence) embedding $\hat{c}_i^-$. These are then fed to a common scoring function *s* estimating the presence probability $\hat{p}_i$ of concept *i*. Embedding vectors $\hat{c}_i^+$ and $\hat{c}_i^-$ are then combined into a final concept embedding $\hat{c}_i = \hat{p}_i\hat{c}_i^+ + (1 - \hat{p}_i)\hat{c}_i^-$. All concept embeddings $\hat{c}_i$ are then concatenated and passed to a final predictor function *f*, which outputs the predicted label $\hat{y}$ for the input. It is worth noting that $\hat{p}_i$ can alternatively be set by an expert's intervention.

On the CUB and CelebA datasets, CEM was trained using a ResNet-34 backbone for $\psi$. For MObyGaze, we use X-CLIP [18] as $\psi$, due to the limited number of available samples, this component is kept frozen. The concept classification heads *s* are implemented as MLPs in all 3 cases.

**Losses:** The model is trained end-to-end by optimizing a combination of a loss on the objectification decision $\hat{y}$ (w.r.t. true label *y*), and a loss on the detections of concepts $\hat{p}_i$ (w.r.t. true labels $p_i$). The relative importance of the concept detection loss can be controlled by a parameter $\alpha$: we optimize $\mathcal{L}_{\text{task}}(y, \hat{y}) + \alpha \sum_i \mathcal{L}_{\text{concept}}(p_i, \hat{p}_i)$. Both $\mathcal{L}_{\text{task}}$ and $\mathcal{L}_{\text{concept}}$ are cross-entropy losses.

**Interpretability Metric:** For the interpretability metric we report the Concept Alignment Score (CAS), originally introduced to quantify CEM interpretability [28]. This metric measures the homogeneity of predicted concept labels within groups of samples which have close concept representations $\hat{c}_i$. If similar samples share the
same concept label, CAS will be high, indicating a latent space cap-
turing and shaped by concepts, that is, by interpretable explanation.

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 1:** (a) Accuracy-Interpretability trade-off for 2 image datasets honed for concept based models (CUB and CelebA) and a new dataset for video interpretation (MObyGaze). (b) Comparison of the datasets on the concept detection difficulty (x-axis) and determination of the end task (y-axis).</figcaption></figure>

## 3.2 Re-examining CEM performance by characterizing the legacy and MOByGaze datasets

**Dataset settings:** CEM has been evaluated on highly curated datasets like CUB and CelebA. Here we analyse CEM on a more challenging dataset, MObyGaze, and show that CEM performs poorly. We highlight specific dimensions in which MObyGaze differ from CUB and CelebA (particularly in the difficulty of concept detection), thereby unveiling failure modes of CEM. To fairly compare with the image-only datasets in this part, we consider only the visual concepts of MObyGaze. The MObyGaze dataset provides 3 labels for the objectification level: The dataset is divided into three categories: Easy Negative (EN), where no concepts are annotated; Hard Negative (HN), where some concepts are present but are considered insufficient to trigger a perception of objectification; and Sure (S), where objectification is clearly perceived and can be fully explained by the annotated concepts. We can therefore consider **three different classification tasks on both vision-only ObyGaze or**

**MObyGaze.** These tasks are associated with different level of difficulties: EN vs HN ∪ S: representing the easiest case where a sample is positive if at least one concept is detected. EN ∪ HN vs S: where HN samples are now in the negative class, which increases the difficulty since annotated concepts are also present in HN samples. EN vs HN vs S: multi-class scenario. These three experimental settings enable a systematic analysis of CEM's performance across varying levels of difficulty, allowing us to precisely identify which component constitutes the primary bottleneck when applied to this challenging dataset.

**CEM perfomance:** Fig. 1.a shows the accuracy-interpretability trade-off of the original CEM on CUB, CelebA and MObyGaze dataset. To have a fair comparison between the 3 datasets with different number of classes, we report the gain in accuracy compared to a random predictor instead of the simple accuracy. For the interpretability we use the CAS metric (see Sec.3.1) that measures how much concept representation reflects their ground-truth concepts.

CEM achieves the highest gain in accuracy on CUB (+1.5×10⁴%) and CelebA (+7.6 × 10³%), while maintaining a high level of interpretability (0.79). On MObyGaze, CEM achieves the lowest gain in accuracy (+35%), with the lower interpretability (only 0.6). This **shows that CEM may not be well-suited for datasets like MObyGaze. We explore two possible reasons for the failure on MObyGaze.**

**Concept Detection difficulty and end task determination assumptions:** First, concept may typically be harder to detect in the video interpretation dataset MObyGaze. This is confirmed with Fig. 1.b that shows the concept detection performance on the x-axis. We compute the F1 score on the outputs of the concept detectors of the CEM. We observe that, while CEM achieves good concept detection on CUB and CelebA, its detection performance on MObyGaze is significantly lower. While concepts are defined with an interpretative component, hence harder to detect as discussed in Sec. 1, CEM concept detectors perform more poorly than concept detectors trained outside CEM. Indeed, the concepts are more complex and scarcely present. This means that for underrepresented concepts such as Expression of Emotion, or Activities, simply weighting the concept loss is not sufficient. **This is why training detectors outside of CEM could be more effective, as it allows for targeted oversampling of each individual concept.**

*Definition 3.1.* An end task is fully determined by its concepts if having a perfect knowledge of the concepts present in a sample determinisitically determine the end task label of the sample.

Secondly, we observe that the bird species classification task of the CUB dataset is fully determined by its concepts. Indeed, because the provided concepts were noisy, the authors of CBM [13] decided to denoise concepts with a majority voting, e.g., if more than 50% of crows have black wings in the data, all crows will be annotated with the concept black wing. This post-processing choice is kept in CEM [28] as well. The fact that the end task of species detection is fully determined by its concepts is further demonstrated on the y-axis of Fig.1.b showing the performance of a CBM model trained on the ground-truth concepts label (and not the predicted concepts),
allowing us to measure the determination of the end task by the concepts.

Similarly, the EN vs HN ∪ S of MObyGaze dataset is fully deter-
mined since detecting one concept implies that the video is either
HN, either S. On the contrary, the task associated with the CelebA
dataset and tasks EN ∪ HN ∪ S and EN ∪ HN vs S of ObyGaze
are not fully determined. For under-determined tasks and concepts
that are hard to detect, relying solely on concept representation is
not sufficient to perform the final task (e.g., objectification classi-
fication). For instance, in tasks like species detection in the CUB
dataset, it seems reasonable to assume that using only concept in-
formation can be enough to achieve good performance on the final
task, since concepts are well detected and knowing the concepts is
essentially equivalent to knowing the final label. However, when
concept detection is more difficult and the task is not fully defined
by its concepts, this assumption becomes too restrictive, as we have
demonstrated above.

## 3.3 Options to alleviate the weaknesses of legacy CEM: CPT, CPT-F and CPT-FD

In light of the above analysis, we make CEM evolve with three dif-
ferent models introduced below: challenging concept detection is
addressed with Concept Pre-trained Transformer (CPT) that can be
kept frozen (CPT-F), while task under-determination is addressed
with decoupled tunable tokens (CPT-FD).

**Re-examining the end-to-end training assumption with Concept Detection Pre-training.** We propose mitigating the issue of difficult concept detection in video-interpretation datasets by pre-training transformer-based concept detectors. Fig. 2.b depicts our first model: **Concept Pre-trained Transformer (CPT)**. Each concept detector is composed by transformer-based layers [24], referred to as $T_i$ in Fig. 2.b, and a classification head composed of a Linear Layer, referred to as $s_i$. Following the CEM framework learning distinct representations for the presence and absence of concepts, we encode the input feature representation $h$, using two classification tokens (CLS tokens) [8]: $\hat{c}_i^+$ and $\hat{c}_i^-$. Hence, we replace in Fig. 2.a the MLPs with transformer layers and the scoring function $s$ with the classification head named $s_i$. Each such module is pre-trained to detect its corresponding concept.

Since concepts are scarcely represented, detectors may diverge
in certain cases when integrated into the CPT model. To prevent
this we also propose the frozen version of CPT: **Concept Pre-
trained Transformer - Frozen (CPT-F)**. This version consists of
freezing $T_i$ and the two classification tokens (CLS tokens) $\hat{c}_i^+$ and $\hat{c}_i^-$.

**Re-examining the sufficiency of concept representations for the end task assumption by decoupling concept classification from explainability tokens.**

We have identified that CEM achieves higher accuracy on the
end task when it is fully determined by the concepts, like in the
CUB case. This suggests that the information contained in the con-
cept embeddings alone is insufficient for optimal performance on
the end task. To address this limitation, building upon our CPT
and CPT-F model, we decouple the embeddings used for concept
detection from those for the end task. The architecture of our new

model, **CPT-FD (CPT-Frozen Decoupled)**, is shown in Fig. 2.c. In
CPT-FD, the concept detector is pre-trained with a CLS token $c_d$
dedicated to the concept detection. After pre-training, we concate-
nate two additional learnable CLS tokens, representing respectively
absence and presence of the concept, $\hat{c}_i^+$ and $\hat{c}_i^-$ meant to capture
the necessary information from the input, unconstrained by rele-
vant features for concept detection, to improve the performance on
the end task.

We have introduced CPT and its frozen version CPT-F, and CPT-FD, our three architectures addressing the challenges of concept detection and the limited availability of supplementary information for the end task of CEM. In the next section, we evaluate our models and compare their performance with existing approaches.

4 Experiments

In this section we investigate experimentally how our models CPT,
CPT-F and CPT-FD improve the accuracy-interpretability trade-
off for a complex video task, and how much of the improvement
depends on the task definition and dataset modalities.

**Dataset** We evaluate our models CPT, CPT-F and CPT-FD on the ObyGaze dataset and the MObyGaze dataset across the three tasks introduced in Sec. 3.2: EN ∪ HN vs S (easiest), EN vs HN ∪ S, EN vs HN vs S and EN ∪ HN vs S.

**Implementation** We use features extractor: X-CLIP [18] for video frames, BERT [8] for text and AST [7] for audio. We adopt a five-fold cross-validation strategy over the 20 MObyGaze movies, where each fold reserves 4 movies for testing, 2 for validation, and uses the remaining 14 for training. Each experiment is repeated with 3 different random seeds, and the final results are reported as the average and standard deviation across all folds and seeds. For model training, we use Adam optimizer [11], with a decreasing learning rate of 1e-5. We stop the training, if no improvement on the validation is seen after 15 epochs, using a batch size of 32. Each transformer block $T_i$ in Fig. 2 consists of two multi-head self-attention layers with eight heads each. Each multi-head self-attention layer is followed by a multilayer perceptron (MLP) comprising two linear layers with a hidden dimension of 2048.

**Pre-training** Each concept block ($T_i$ and $s_i$ shown in Fig. 2) is pre-trained using oversampling of the positive class for a maximum of 100 epochs, with early stopping based on validation loss (patience of 15), a learning rate of 1e-5 and binary cross-entropy as the loss function and a batch size of 32.

**State-of-the-art concept-based models** We compare our models CPT, CPT-F and CPT-FD to standard concept-based methods: CBM, CEM, CEM-NCS.

* CBM [13] is implemented with pretrained transformer layers on each concept detection task. Then we construct a vector of size *n* (i.e, the number of concepts), that contains the prediction of each transformer layers, then this prediction is given to an MLP that performs the objectification detection task.

* CEM and CEM-NCS are from [28], CEM corresponds to the legacy architecture with $\alpha = 1$ in the training loss, and CEM-NCS (No Concept Supervision) to CEM with $\alpha = 0$, that is when the model is not trained for concept classification,
<figure><img src="image_7.png" /><figcaption>Figure 2: Comparison of different concept-based models: (a) Original CEM, (b) our Concept Pre-trained transformer (CPT) for concept detection and its Frozen version (CPT-F), (c) our Concept Pre-trained transformer Frozen with Decoupled CLS token for the end task (CPT-FD)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 3: Accuracy-Interpretability trade-off in terms of AUC-PR and CAS. Results on the ObyGaze dataset (visual concepts only). One graph for each classification task.</figcaption></figure>

hence leaving all freedom to maximize performance on the end task (see Sec. 3.1).

**Metrics** In improving the accuracy-interpretability trade-off for which CEM has been introduced, we define the accuracy as the area under the precision-recall curve (AUC-PR) for the end task of objectification detection as it is agnostic to thresholding and accounts for class imbalance. To evaluate the interpretability of the learnt concept representation, we use the Concept Alignment Score (CAS), which is described in more detail in Sec. 3.1.

## 4.1 Comparison to standard concept-based methods

**Vision-only concepts:** In the following, models are trained and evaluated using the ObyGaze dataset using seven visual concepts that, despite all pertaining to the visual modality, are heterogeneous in nature: Look and Type of Shot concern filmic aspects (involving camera positioning and movement), while Activities, Body, Clothing, Expression of an emotion and Posture are iconographic.
Fig. 3, shows the **End task performance - Interpretability trade-off** for the ObyGaze dataset with one graph per task.

Each color dot represents a model. As CAS is a metric of concept class homogeneity over clusters in the latent space, and CBM does not project the concepts onto a latent space, we show it as a red line.

Fig. 3.a shows the trade-off between the performance on the end task and interpretability for the easiest EN vs HN ∪ S task, where the end task is entirely determined by the concepts. We observe that all methods show performance comparable to CBM in terms of AUC-PR. This result supports our design hypothesis that, when the end task is fully determined by the concepts, fine-tuning pre-trained concept detectors for the end task, does not improve performance on this task (CPT and CPT-FD do not exceed CPT-F in AUC-PR). Also, we observe that our proposed CPT-F, achieves the highest CAS, hence improving interpretability by more than 6% compared to CEM.

**This shows that, for the determined case with vision-only concepts:**

* maximum concept alignment is obtained by pre-trained concept detectors (CPT-F),

* while fine-tuning for the final task degrades concept alignment (CPT-FD).

Fig. 3.b and 3.c show the accuracy-interpretability trade-off for the harder tasks EN ∪ HN vs S and EN vs HN vs S, respectively. These two end tasks are not fully determined by the concepts. In Fig. 3.b we observe that the two best-performing methods in terms of AUC-PR are CEM-NCS (0.52) and our model CPT-FD (0.545), which exceeds original CEM and CPT by 17%. These two methods enable the model to incorporate additional information beyond what is necessary for concept prediction: CEM-NCS by not constraining the model at all regarding the concepts; and our proposed CPT-FD by concatenating two additional tokens specifically for the end task. In Fig. 3.c, our method CPT-FD achieves the highest AUC-PR (0.403), exceeding CEM and CPT by 10%.

**This shows that, for the under-determined case with vision-only concepts:**

* CEM does not allow for the latent space to capture visual information from the input beyond what is needed for concept detection.

* In contrast, our CPT-FD model, thanks to its decoupled CLS, addresses this need and improves performance on the ObyGaze dataset.

* In all tasks, our CPT-F model significantly improves interpretability.

**Multimodal concepts:** We train and evaluate the models with the MObyGaze dataset using seven visual concepts, both audio concepts (Voice and Soundtrack) and the one textual concept (Speech). Fig. 4 shows the accuracy-interpretability trade-off for the EN ∪ HN vs S task using visual, textual, and audio concepts. In the previous vision-only setting, the frozen versions CPT-F and CPT-FD that previously performed better in terms of CAS and AUC-PR, respectively. In this setting, however, CPT surpasses both CPT-F and CPT-FD on both axes. This could be explained by the fact that Speech and Voice (two of the three additional concepts) are among

<figure><img src="image_9.png" /><figcaption>**Figure 4: Accuracy-Interpretability trade-off in terms of AUC-PR and CAS. Results on the MObyGaze dataset with 10 concepts and 3 modalities.**</figcaption></figure>

the most represented and help the model learn effective representations of the concepts. The best-performing model, in terms of AUC-PR, in the vision-only setting was CPT-FD (Fig. 3.b), with a score of 0.55, while the worst-performing model in the multimodal setting, also CPT-FD, achieved a higher score of 0.59.

**This highlights that the optimal CEM-type approach varies depending on the modalities involved, which shows that designing concept-based models for multimodal data is still an open problem.**

## 4.2 Ablation study

In this section, we perform an ablation study, for the ObyGaze dataset, to assess the impact of two key components in our CPT-FD model: the use of a frozen pre-trained models for the concept detection task, and the separation of features dedicated to the end task ($\hat{c}_i^+$ and $\hat{c}_i^-$) and those dedicated to the concept class ($\hat{c}_i^d$). These elements are highlighted in Fig. 2.c in the blue ellipses and make for the following variants from CPT-FD (Ours): CPT-FDA, CPT-FAD, and CPT-FADA. CPT-FDA does not concatenate to the transformer input and tune both extra tokens $\hat{c}_i^+$ and $\hat{c}_i^-$, instead it splits $\hat{c}_i^d$ token in half. The first half corresponds to $\hat{c}_i^+$ and second half to $\hat{c}_i^-$. CPT-FAD uses a trainable scoring function $s_i$, while CPT-FADA combines both of the previous ablations.

Table 1 shows the performance on both AUC-PR and CAS of the models on the EN ∪ HN vs S task. Our proposed model, CPT-FD, achieves the highest AUC-PR (0.55). Using coupled features in CPT-FDA results in the biggest drop of 23% in AUC-PR. The most important component is therefore the decoupling of embeddings between concept detection and end task.

The impact is also significant on CAS (4% increase from CPT-FDA to CPT-FD). When embeddings are not decoupled between concept detection and task, enabling $s_i$ to get fine-tuned allow to partly recover on AUC-PR (CPT-FAD).
<table><caption>Table 1: Ablation study on the ENUHN vs S task.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Metric</th><th colspan="2">Caracteristic</th></tr><tr><th>AUC-PR</th><th>CAS</th><th>Decoupled CLS</th><th>Frozen concept detector</th></tr></thead><tbody><tr><td>CPT-FD (Ours)</td><td>0.55 (0.01)</td><td>61.5 (0.288)</td><td>✓</td><td>✓</td></tr><tr><td>CFPT-FDA</td><td>0.44 (0.01)</td><td>58.4 (0.13)</td><td>✗</td><td>✓</td></tr><tr><td>CPT-FAD</td><td>0.51 (0.03)</td><td>61.0 (0.56)</td><td>✓</td><td>✗</td></tr><tr><td>CPT-FADA</td><td>0.51 (0.01)</td><td>58.7 (0.25)</td><td>✗</td><td>✗</td></tr></tbody></table>

Our ablation study demonstrates that, in the vision-only setting, the two key components in our model CPT-FD, a frozen pre-trained concept detector and decoupled features for concept detection and end tasks, contribute to achieving higher AUC-PR and CAS.

## 5 Conclusion

In this work, we aim to use explainable models on the complex video interpretation dataset MObyGaze, but these models are proving very difficult to use on this complex dataset. Our Brave New Idea is therefore to conduct a root cause analysis of the poor performances of state-of-the-art explainable concept-based approaches exploiting concepts for multimodal interpretative tasks. We investigated how to design concept-based models for multimodal interpretative video data by conducting an extensive analysis of the CEM model on two curated datasets (CUB and CelebA) and a more challenging video-interpretation dataset (MObyGaze). We identified two limitations of the CEM architecture: the end-to-end training strategy prevents effective concept detection and the concept representations that are not sufficient to achieve good results on the downstream task when the concepts do not fully determine this downstream task. To mitigate these limitations we proposed three key adaptations, of the ways concepts are detected and used in concept embedding models. We assessed the performance of our models CPT, CPT-F and CPT-FD, proving the usefulness of our proposed adaptations, but also importantly highlighting that the optimal architectural choice may vary depending on the modality setting. Achieving multimodal explainability using prominent concept-based approaches such as CEM therefore remains an open challenge and calls for further investigation.

**Acknowledgements.** This work has been partly supported by the French National Research Agency through the ANR TRACTIVE project ANR-21-CE38-0012-01. This work was partly supported by EU Horizon 2020 project AI4Media, under contract no. 951911 (https://ai4media.eu/).

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