Miracle-dz/UniFAR-FTUs huggingface dataset:
Trailers12k : Evaluating Transfer Learning for Movie Trailer Genre Classiﬁcation

Transfer learning is a cornerstone for a wide range of computer vision problems. It has been broadly studied for image analysis tasks. However, literature for video analysis is scarce and has been mainly focused on transferring representations learned from ImageNet to human action recognition tasks. In this paper, we study transfer learning for Multi-label Movie Trailer Genre Classiﬁcation (MTGC). In particular, we introduce Trailers12k , a new manually-curated movie trailer dataset and evaluate the transferability of spatial and spatio-temporal representations learned from ImageNet and/or Kinetics to Trailers12k MTGC. In order to reduce the spatio-temporal structure gap between the source and target tasks and improve transferability, we pro-pose a method that performs shot detection so as to segment the trailer into highly correlated clips. We study different aspects that inﬂuence transferability, such as segmentation strategy, frame rate, input video extension, and spatio-temporal modeling. Our results demonstrate that representations learned on either ImageNet or Kinetics are comparatively transferable to Trailers12k, although they provide complementary information that can be combined to improve classiﬁcation performance. Having a similar number of parameters and FLOPS, Transformers provide a better transferability base than ConvNets. Nevertheless, competitive performance can be achieved using lightweight ConvNets, becoming an attractive option for low-resource environments.
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning

Multi-agent reinforcement learning has drawn increasing attention in practice, e.g., robotics and automatic driving, as it can explore optimal policies using samples generated by interacting with the environment. However, high reward uncertainty still remains a problem when we want to train a satisfactory model, because obtaining high-quality reward feedback is usually expensive and even infeasible. To handle this issue, previous methods mainly focus on passive reward correction. At the same time, recent active reward estimation methods have proven to be a recipe for reducing the effect of reward uncertainty. In this paper, we propose a novel Distributional Reward Estimation framework for effective Multi-Agent Reinforcement Learning (DRE-MARL). Our main idea is to design the multi-action-branch reward estimation and policy-weighted reward aggregation for stabilized training. Specifically, we design the multi-action-branch reward estimation to model reward distributions on all action branches. Then we utilize reward aggregation to obtain stable updating signals during training. Our intuition is that consideration of all possible consequences of actions could be useful for learning policies. The superiority of the DRE-MARL is demonstrated using benchmark multi-agent scenarios, compared with the SOTA baselines in terms of both effectiveness and robustness.
Hierarchical Policy Blending as Inference for Reactive Robot Control

Motion generation in cluttered, dense, and dynamic environments is a central topic in robotics, rendered as a multi-objective decision-making problem. Current approaches trade-off between safety and performance. On the one hand, reactive policies guarantee a fast response to environmental changes at the risk of suboptimal behavior. On the other hand, planning-based motion generation provides feasible trajectories, but the high computational cost may limit the control frequency and, thus, safety. To combine the benefits of reactive policies and planning, we propose a hierarchical motion generation method. Moreover, we employ probabilistic inference methods to formalize the hierarchical model and stochastic optimization. We realize this approach as a weighted product of stochastic, reactive expert policies, where planning is used to adaptively compute the optimal weights over the task horizon. This stochastic optimization avoids local optima and proposes feasible reactive plans that find paths in cluttered and dense environments. Our extensive experimental study in planar navigation and 7DoF manipulation shows that our proposed hierarchical motion generation method outperforms both myopic reactive controllers and online re-planning methods. Additional material available at https://sites.google. com/view/hipbi.
Distributed Computation for the Non-metric Data Placement Problem using Glauber Dynamics and Auctions

We consider the non-metric data placement problem and develop distributed algorithms for computing or approximating its optimal integral solution. We first show that the non-metric data placement problem is inapproximable up to a logarithmic factor. We then provide a game-theoretic decomposition of the objective function and show that natural Glauber dynamics in which players update their resources with probability proportional to the utility they receive from caching those resources will converge to an optimal global solution for a sufficiently large noise parameter. In particular, we establish the polynomial mixing time of the Glauber dynamics for a certain range of noise parameters. Finally, we provide another auction-based distributed algorithm, which allows us to approximate the optimal global solution with a performance guarantee that depends on the ratio of the revenue vs. social welfare obtained from the underlying auction. Our results provide the first distributed computation algorithms for the non-metric data placement problem.
Adaptive extremum seeking control of nonisothermal continuous stirred tank reactors with temperature constraints

In this paper, we present an adaptive extremum seeking control scheme for nonisothermal continuous stirred tank reactors subject to reactor temperature constraints. Only limited knowledge of the reaction kinetics is assumed with no direct measurement of the reaction mixture composition. An adaptive learning technique is introduced to construct an optimum seeking algorithm that drives the system states to optimal equilibrium concentrations of the reaction mixture taking into account reactor temperature constraints. Lyapunov's stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. Under mild assumptions, the resulting controller is an output-feedback controller. The performance of the technique is demonstrated with the van de Vusse reaction.
Skill-Based Reinforcement Learning with Intrinsic Reward Matching

While unsupervised skill discovery has shown promise in autonomously acquiring behavioral primitives, there is still a large methodological disconnect between task-agnostic skill pretraining and downstream, task-aware finetuning. We present Intrinsic Reward Matching (IRM), which unifies these two phases of learning via the $\textit{skill discriminator}$, a pretraining model component often discarded during finetuning. Conventional approaches finetune pretrained agents directly at the policy level, often relying on expensive environment rollouts to empirically determine the optimal skill. However, often the most concise yet complete description of a task is the reward function itself, and skill learning methods learn an $\textit{intrinsic}$ reward function via the discriminator that corresponds to the skill policy. We propose to leverage the skill discriminator to $\textit{match}$ the intrinsic and downstream task rewards and determine the optimal skill for an unseen task without environment samples, consequently finetuning with greater sample-efficiency. Furthermore, we generalize IRM to sequence skills for complex, long-horizon tasks and demonstrate that IRM enables us to utilize pretrained skills far more effectively than previous skill selection methods on both the Fetch tabletop and Franka Kitchen robot manipulation benchmarks.
Learning To Rank Diversely At Airbnb

Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.
Expert Systems With Applications

Localization algorithms that rely on 3D LiDAR scanners often encounter temporary failures due to various factors, such as sensor faults, dust particles, or drifting. These failures can result in a misalignment between the robot’s estimated pose and its actual position in the global map. To address this issue, the process of global re-localization becomes essential, as it involves accurately estimating the robot’s current pose within the given map. In this article, we propose a novel global re-localization framework that addresses the limitations of current algorithms heavily reliant on scan matching and direct point cloud feature extraction. Unlike most methods, our framework eliminates the need for an initial guess and provides multiple top-𝑘 candidates for selection, enhancing robustness and flexibility. Furthermore, we introduce an event-based re-localization trigger module, enabling autonomous robotic missions. Focusing on subterranean environments with low features, we leverage range image descriptors derived from 3D LiDAR scans to preserve depth information. Our approach enhances a state-of-the-art data-driven descriptor extraction framework for place recognition and orientation regression by incorporating a junction detection module that utilizes the descriptors for classification purposes. The effectiveness of the proposed approach was evaluated across three distinct real-life subterranean environments.
Early Discovery of Disappearing Entities in Microblogs

We make decisions by reacting to changes in the real world, particularly the emergence and disappearance of impermanent entities such as restaurants, services, and events. Because we want to avoid missing out on opportunities or making fruitless actions after those entities have disappeared, it is important to know when entities disappear as early as possible.We thus tackle the task of detecting disappearing entities from microblogs where various information is shared timely.The major challenge is detecting uncertain contexts of disappearing entities from noisy microblog posts.To collect such disappearing contexts, we design time-sensitive distant supervision, which utilizes entities from the knowledge base and time-series posts.Using this method, we actually build large-scale Twitter datasets of disappearing entities.To ensure robust detection in noisy environments, we refine pretrained word embeddings for the detection model on microblog streams in a timely manner.Experimental results on the Twitter datasets confirmed the effectiveness of the collected labeled data and refined word embeddings; the proposed method outperformed a baseline in terms of accuracy, and more than 70% of the detected disappearing entities in Wikipedia are discovered earlier than the update on Wikipedia, with the average lead-time is over one month.
Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets

Creating separable representations via representation learning and clustering is critical in analyzing large unstructured datasets with only a few labels. Separable representations can lead to supervised models with better classification capabilities and additionally aid in generating new labeled samples. Most unsupervised and semisupervised methods to analyze large datasets do not leverage the existing small amounts of labels to get better representations. In this paper, we propose a spatiotemporal clustering paradigm that uses spatial and temporal features combined with a constrained loss to produce separable representations. We show the working of this method on the newly published dataset ReaLSAT, a dataset of surface water dynamics for over 680,000 lakes across the world, making it an essential dataset in terms of ecology and sustainability. Using this large unlabelled dataset, we first show how a spatiotemporal representation is better compared to just spatial or temporal representation. We then show how we can learn even better representation using a constrained loss with few labels. We conclude by showing how our method, using few labels, can pick out new labeled samples from the unlabeled data, which can be used to augment supervised methods leading to better classification.
Beamforming Technologies for Ultra-Massive MIMO in Terahertz Communications

Terahertz (THz) communications with a frequency band 0.1 – 10 THz are envisioned as a promising solution to future high-speed wireless communication. Although with tens of gigahertz available bandwidth, THz signals suffer from severe free-spreading loss and molecular-absorption loss, which limit the wireless transmission distance. To compensate for the propagation loss, the ultra-massive multiple-input-multiple-output (UM-MIMO) can be applied to generate a high-gain directional beam by beamforming technologies. In this paper, a review of beamforming technologies for THz UM-MIMO systems is provided. Specifically, we first present the system model of THz UM-MIMO and identify its channel parameters and architecture types. Then, we illustrate the basic principles of beamforming via UM-MIMO and discuss the far-field and near-field assumptions in THz UM-MIMO. Moreover, an important beamforming strategy in THz band, i.e., beam training, is introduced wherein the beam training protocol and codebook design approaches are summarized. The intelligent-reflecting-surface (IRS)-assisted joint beamforming and multi-user beamforming in THz UM-MIMO systems are studied, respectively. The spatial-wideband effect and frequency-wideband effect in the THz beamforming are analyzed and the corresponding solutions are provided. Further, we present the corresponding fabrication techniques and illuminate the emerging applications benefiting from THz beamforming. Open challenges and future research directions on THz UM-MIMO systems are finally highlighted.
Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset

In this paper we present a three-stream algorithm for real-time action recognition and a new dataset of handwash videos, with the intent of aligning action recognition with real-world constraints to yield effective conclusions. A three-stream fusion algorithm is proposed, which runs both accurately and efficiently, in real-time even on low-powered systems such as a Raspberry Pi. The cornerstone of the proposed algorithm is the incorporation of both spatial and temporal information, as well as the information of the objects in a video while using an efficient architecture, and Optical Flow computation to achieve commendable results in real-time. The results achieved by this algorithm are benchmarked on the UCF-101 as well as the HMDB-51 datasets, achieving an accuracy of 92.7% and 64.9% respectively. An important point to note is that the algorithm is novel in the aspect that it is also able to learn the intricate differences between extremely similar actions, which would be difficult even for the human eye. Additionally, noticing a dearth in the number of datasets for the recognition of very similar or fine-grained actions, this paper also introduces a new dataset that is made publicly available, the Hand Wash Dataset with the intent of introducing a new benchmark for fine-grained action recognition tasks in the future.
Augmenting Online Classes with an Attention Tracking Tool May Improve Student Engagement

Online remote learning has certain advantages, such as higher flexibility and greater inclusiveness. However, a caveat is the teachers' limited ability to monitor student interaction during an online class, especially while teachers are sharing their screens. We have taken feedback from 12 teachers experienced in teaching undergraduate-level online classes on the necessity of an attention tracking tool to understand student engagement during an online class. This paper outlines the design of such a monitoring tool that automatically tracks the attentiveness of the whole class by tracking students' gazes on the screen and alerts the teacher when the attention score goes below a certain threshold. We assume the benefits are twofold; 1) teachers will be able to ascertain if the students are attentive or being engaged with the lecture contents and 2) the students will become more attentive in online classes because of this passive monitoring system. In this paper, we present the preliminary design and feasibility of using the proposed tool and discuss its applicability in augmenting online classes. Finally, we surveyed 31 students asking their opinion on the usability as well as the ethical and privacy concerns of using such a monitoring tool.
ON INCREMENTALITY, OVERT AGREEMENT, THE DUALITY OF MERGE AND THE DUALITY OF SEMANTICS

With regard to (i), the crosslinguistic facts mentioned above were originally taken to constitute an argument for grammatically significant Spec,Head configurations, that is to say, for the existence of checking relations under m-command. These relations are, nonetheless, at odds with the phase-based framework. In this paper, it is argued that incremental computation of Agreement (cf. Barlow 1992) explains such paradigms in a way that is compatible with Long-Distance Agreement (Chomsky 2001, etc.). As far as (ii) is concerned, Chomsky (2004 and 2006) proposes that External Merge or movement is associated to discourse-related and scopal properties as opposed to theta roles. However, this researcher acknowledges that the correlation is not a logical necessity (Chomsky 2004: 11). It is argued that such mapping does not have to be stipulated but rather it can be derived from the workings of Incrementality. Section 1 presents the relevant crosslinguistic generalizations concerning agreement paradigms and the role of Incrementality in this context. Section 2 discusses the mapping between the duality of Merge and the duality of Semantics and, again, the role of Incrementality in this context.
Early stopping for $ L^2 $-boosting in high-dimensional linear models

Increasingly high-dimensional data sets require that estimation methods do not only satisfy statistical guarantees but also remain computationally feasible. In this context, we consider $ L^{2} $-boosting via orthogonal matching pursuit in a high-dimensional linear model and analyze a data-driven early stopping time $ \tau $ of the algorithm, which is sequential in the sense that its computation is based on the first $ \tau $ iterations only. This approach is much less costly than established model selection criteria, that require the computation of the full boosting path. We prove that sequential early stopping preserves statistical optimality in this setting in terms of a fully general oracle inequality for the empirical risk and recently established optimal convergence rates for the population risk. Finally, an extensive simulation study shows that at an immensely reduced computational cost, the performance of these type of methods is on par with other state of the art algorithms such as the cross-validated Lasso or model selection via a high dimensional Akaike criterion based on the full boosting path.
3D GAN Inversion with Pose Optimization

With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion: not only does it allow multi-view consistent editing of the projected image, but it also enables 3D reconstruction and novel view synthesis when given only a single image. However, the explicit viewpoint control acts as a main hindrance in the 3D GAN inversion process, as both camera pose and latent code have to be optimized simultaneously to reconstruct the given image. Most works that explore the latent space of the 3D-aware GANs rely on ground-truth camera viewpoint or deformable 3D model, thus limiting their applicability. In this work, we introduce a generalizable 3D GAN inversion method that infers camera viewpoint and latent code simultaneously to enable multi-view consistent semantic image editing. The key to our approach is to leverage pre-trained estimators for better initialization and utilize the pixel-wise depth calculated from NeRF parameters to better reconstruct the given image. We conduct extensive experiments on image reconstruction and editing both quantitatively and qualitatively, and further compare our results with 2D GAN-based editing to demonstrate the advantages of utilizing the latent space of 3D GANs. Additional results and visualizations are available at https://3dgan-inversion.github. io/.
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?

Recent developments in monocular multi-object tracking have been very successful in tracking visible objects and bridging short occlusion gaps, mainly relying on data-driven appearance models. While we have significantly advanced short-term tracking performance, bridging longer occlusion gaps remains elusive: state-of-the-art object trackers only bridge less than 10% of occlusions longer than three seconds. We suggest that the missing key is reasoning about future trajectories over a longer time horizon. Intuitively, the longer the occlusion gap, the larger the search space for possible associations. In this paper, we show that even a small yet diverse set of trajectory predictions for moving agents will significantly reduce this search space and thus improve long-term tracking robustness. Our experiments suggest that the crucial components of our approach are reasoning in a bird's-eye view space and generating a small yet diverse set of forecasts while accounting for their localization uncertainty. This way, we can advance state-of-the-art trackers on the MOTChallenge dataset and significantly improve their long-term tracking performance. This paper's source code and experimental data are available at https://github.com/dendorferpatrick/QuoVadis.