pwc-archive/papers-with-abstracts huggingface dataset:
Panoramic Human Activity Recognition
Authors: Ruize Han, Haomin Yan, Jiacheng Li, Songmiao Wang, Wei Feng, Song Wang
Date: 2022-03-08 00:00:00
Tasks: Activity Recognition, Graph Neural Network, Human Activity Recognition

To obtain a more comprehensive activity understanding for a crowded scene, in this paper, we propose a new problem of panoramic human activity recognition (PAR), which aims to simultaneous achieve the individual action, social group activity, and global activity recognition. This is a challenging yet practical problem in real-world applications. For this problem, we develop a novel hierarchical graph neural network to progressively represent and model the multi-granularity human activities and mutual social relations for a crowd of people. We further build a benchmark to evaluate the proposed method and other existing related methods. Experimental results verify the rationality of the proposed PAR problem, the effectiveness of our method and the usefulness of the benchmark. We will release the source code and benchmark to the public for promoting the study on this problem.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Authors: Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, Eugene Belilovsky
Date: 2022-03-08 00:00:00
Proceeding: ICLR 2022 4
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning

In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes' representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks
Battery Cloud with Advanced Algorithms
Authors: Xiaojun Li, David Jauernig, Mengzhu Gao, Trevor Jones
Date: 2022-03-07 00:00:00
Tasks: Anomaly Detection, Management

A Battery Cloud or cloud battery management system leverages the cloud computational power and data storage to improve battery safety, performance, and economy. This work will present the Battery Cloud that collects measured battery data from electric vehicles and energy storage systems. Advanced algorithms are applied to improve battery performance. Using remote vehicle data, we train and validate an artificial neural network to estimate pack SOC during vehicle charging. The strategy is then tested on vehicles. Furthermore, high accuracy and onboard battery state of health estimation methods for electric vehicles are developed based on the differential voltage (DVA) and incremental capacity analysis (ICA). Using cycling data from battery cells at various temperatures, we extract the charging cycles and calculate the DVA and ICA curves, from which multiple features are extracted, analyzed, and eventually used to estimate the state of health. For battery safety, a data-driven thermal anomaly detection method is developed. The method can detect unforeseen anomalies such as thermal runaways at the very early stage. With the further development of the internet of things, more and more battery data will be available. Potential applications of battery cloud also include areas such as battery manufacture, recycling, and electric vehicle battery swap.
HyperMixer: An MLP-based Low Cost Alternative to Transformers
Authors: Florian Mai, Arnaud Pannatier, Fabio Fehr, Haolin Chen, Francois Marelli, Francois Fleuret, James Henderson
Date: 2022-03-07 00:00:00
Tasks: Natural Language Understanding

Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input length, require a lot of training data, and can be difficult to tune. In the pursuit of lower costs, we investigate simple MLP-based architectures. We find that existing architectures such as MLPMixer, which achieves token mixing through a static MLP applied to each feature independently, are too detached from the inductive biases required for natural language understanding. In this paper, we propose a simple variant, HyperMixer, which forms the token mixing MLP dynamically using hypernetworks. Empirically, we demonstrate that our model performs better than alternative MLP-based models, and on par with Transformers. In contrast to Transformers, HyperMixer achieves these results at substantially lower costs in terms of processing time, training data, and hyperparameter tuning.
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
Authors: Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
Date: 2022-03-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market. At each step, the agents are presented with a dynamical context, where the contexts determine the utilities. The planner controls the transition of the contexts to maximize the cumulative social welfare, while the agents aim to find a myopic stable matching at each step. Such a setting captures a range of applications including ridesharing platforms. We formalize the problem by proposing a reinforcement learning framework that integrates optimistic value iteration with maximum weight matching. The proposed algorithm addresses the coupled challenges of sequential exploration, matching stability, and function approximation. We prove that the algorithm achieves sublinear regret.
Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers
Authors: Miguel Saavedra-Ruiz, Sacha Morin, Liam Paull
Date: 2022-03-07 00:00:00
Tasks: CPU, Image Segmentation, Robot Navigation, Segmentation, Semantic Segmentation

In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images. Using a Vision Transformer (ViT) pretrained with a label-free self-supervised method, we successfully train a coarse image segmentation model for the Duckietown environment using 70 training images. Our model performs coarse image segmentation at the 8x8 patch level, and the inference resolution can be adjusted to balance prediction granularity and real-time perception constraints. We study how best to adapt a ViT to our task and environment, and find that some lightweight architectures can yield good single-image segmentation at a usable frame rate, even on CPU. The resulting perception model is used as the backbone for a simple yet robust visual servoing agent, which we deploy on a differential drive mobile robot to perform two tasks: lane following and obstacle avoidance.
Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategies
Authors: Andrei Dmitrenko, Mauro M. Masiero, Nicola Zamboni
Date: 2022-03-07 00:00:00
Tasks: Contrastive Learning, Representation Learning

Recent advances in computer vision and robotics enabled automated large-scale biological image analysis. Various machine learning approaches have been successfully applied to phenotypic profiling. However, it remains unclear how they compare in terms of biological feature extraction. In this study, we propose a simple CNN architecture and implement 4 different representation learning approaches. We train 16 deep learning setups on the 770k cancer cell images dataset under identical conditions, using different augmenting and cropping strategies. We compare the learned representations by evaluating multiple metrics for each of three downstream tasks: i) distance-based similarity analysis of known drugs, ii) classification of drugs versus controls, iii) clustering within cell lines. We also compare training times and memory usage. Among all tested setups, multi-crops and random augmentations generally improved performance across tasks, as expected. Strikingly, self-supervised (implicit contrastive learning) models showed competitive performance being up to 11 times faster to train. Self-supervised regularized learning required the most of memory and computation to deliver arguably the most informative features. We observe that no single combination of augmenting and cropping strategies consistently results in top performance across tasks and recommend prospective research directions.
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis
Authors: Chengjia Wang, Guang Yang, Giorgos Papanastasiou
Date: 2022-03-07 00:00:00
Tasks: Image Generation, Image Registration, Medical Image Analysis, Unsupervised Image Registration

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non-rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an un-supervised deep learning registration methodology which can accurately model affine and non-rigid trans-formations, simultaneously. Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms. To address inverse-consistency, our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent rep-resentations, while involves two factorised transformation networks and an inverse-consistency loss to learn topology-preserving anatomical transformations. Overall, our model (named "FIRE") shows improved performances against the reference standard baseline method on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI data experiments.
Triple Motion Estimation and Frame Interpolation based on Adaptive Threshold for Frame Rate Up-Conversion
Authors: Hanieh Naderi, Mohammad Rahmati
Date: 2022-03-05 00:00:00
Tasks: Motion Estimation

In this paper, we propose a novel motion-compensated frame rate up-conversion (MC-FRUC) algorithm. The proposed algorithm creates interpolated frames by first estimating motion vectors using unilateral (jointing forward and backward) and bilateral motion estimation. Then motion vectors are combined based on adaptive threshold, in order to creates high-quality interpolated frames and reduce block artifacts. Since motion-compensated frame interpolation along unilateral motion trajectories yields holes, a new algorithm is introduced to resolve this problem. The experimental results show that the quality of the interpolated frames using the proposed algorithm is much higher than the existing algorithms.
A Typology for Exploring the Mitigation of Shortcut Behavior
Authors: Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting
Date: 2022-03-04 00:00:00
Tasks: BIG-bench Machine Learning

As machine learning models become increasingly larger, trained weakly supervised on large, possibly uncurated data sets, it becomes increasingly important to establish mechanisms for inspecting, interacting, and revising models to mitigate learning shortcuts and guarantee their learned knowledge is aligned with human knowledge. The recently proposed XIL framework was developed for this purpose, and several such methods have been introduced, each with individual motivations and methodological details. In this work, we provide a unification of various XIL methods into a single typology by establishing a common set of basic modules. In doing so, we pave the way for a principled comparison of existing, but, importantly, also future XIL approaches. In addition, we discuss existing and introduce novel measures and benchmarks for evaluating the overall abilities of a XIL method. Given this extensive toolbox, including our typology, measures, and benchmarks, we finally compare several recent XIL methods methodologically and quantitatively. In our evaluations, all methods prove to revise a model successfully. However, we found remarkable differences in individual benchmark tasks, revealing valuable application-relevant aspects for integrating these benchmarks in developing future methods.
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions
Authors: Francois St-Hilaire, Dung Do Vu, Antoine Frau, Nathan Burns, Farid Faraji, Joseph Potochny, Stephane Robert, Arnaud Roussel, Selene Zheng, Taylor Glazier, Junfel Vincent Romano, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Tommy Delarosbil, Seulmin Ahn, Simon Eden-Walker, Kritika Sony, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Victor Chen, Hossein Sahraei, Robert Larson, Nadia Markova, Andrew Barkett, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar
Date: 2022-03-03 00:00:00
Tasks: Active Learning, Multiple-choice

Despite artificial intelligence (AI) having transformed major aspects of our society, less than a fraction of its potential has been explored, let alone deployed, for education. AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning. This is especially relevant in the context of online learning platforms. In this paper, we present the results of a comparative head-to-head study on learning outcomes for two popular online learning platforms (n=199 participants): A MOOC platform following a traditional model delivering content using lecture videos and multiple-choice quizzes, and the Korbit learning platform providing a highly personalized, active and practical learning experience. We observe a huge and statistically significant increase in the learning outcomes, with students on the Korbit platform providing full feedback resulting in higher course completion rates and achieving learning gains 2 to 2.5 times higher than both students on the MOOC platform and students in a control group who don't receive personalized feedback on the Korbit platform. The results demonstrate the tremendous impact that can be achieved with a personalized, active learning AI-powered system. Making this technology and learning experience available to millions of learners around the world will represent a significant leap forward towards the democratization of education.
Foundations for Grassroots Democratic Metaverse
Authors: Ehud Shapiro, Nimrod Talmon
Date: 2022-03-02 00:00:00

While the physical lives of many of us are in democracies (one person, one vote - e.g., the EU and the US), our digital lives are mostly in autocracies (one person, all votes - e.g., Facebook). Cryptocurrencies promise liberation but stop short, at plutocracy (one coin, one vote). What would it take for us to live our digital lives in a digital democracy? This paper offers a vision, a theoretical framework, and an architecture for a grassroots network of autonomous, people-owned, people-operated, and people-governed digital communities, namely a grassroots democratic metaverse. It also charts a roadmap towards realizing it, and identifies unexplored territory for further research.
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
Authors: Yutong Chen, Fangyun Wei, Xiao Sun, Zhirong Wu, Stephen Lin
Date: 2022-03-08 00:00:00
Proceeding: CVPR 2022 1
Tasks: Sign Language Recognition, Sign Language Translation, Transfer Learning, Translation

This paper proposes a simple transfer learning baseline for sign language translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily) contain only about 10K-20K pairs of sign videos, gloss annotations and texts, which are an order of magnitude smaller than typical parallel data for training spoken language translation models. Data is thus a bottleneck for training effective sign language translation models. To mitigate this problem, we propose to progressively pretrain the model from general-domain datasets that include a large amount of external supervision to within-domain datasets. Concretely, we pretrain the sign-to-gloss visual network on the general domain of human actions and the within-domain of a sign-to-gloss dataset, and pretrain the gloss-to-text translation network on the general domain of a multilingual corpus and the within-domain of a gloss-to-text corpus. The joint model is fine-tuned with an additional module named the visual-language mapper that connects the two networks. This simple baseline surpasses the previous state-of-the-art results on two sign language translation benchmarks, demonstrating the effectiveness of transfer learning. With its simplicity and strong performance, this approach can serve as a solid baseline for future research. Code and models are available at: https://github.com/FangyunWei/SLRT.
Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences
Authors: Prune Truong, Martin Danelljan, Fisher Yu, Luc van Gool
Date: 2022-03-08 00:00:00
Proceeding: CVPR 2022 1
Tasks: Triplet, Weakly-supervised Learning

We propose Probabilistic Warp Consistency, a weakly-supervised learning objective for semantic matching. Our approach directly supervises the dense matching scores predicted by the network, encoded as a conditional probability distribution. We first construct an image triplet by applying a known warp to one of the images in a pair depicting different instances of the same object class. Our probabilistic learning objectives are then derived using the constraints arising from the resulting image triplet. We further account for occlusion and background clutter present in real image pairs by extending our probabilistic output space with a learnable unmatched state. To supervise it, we design an objective between image pairs depicting different object classes. We validate our method by applying it to four recent semantic matching architectures. Our weakly-supervised approach sets a new state-of-the-art on four challenging semantic matching benchmarks. Lastly, we demonstrate that our objective also brings substantial improvements in the strongly-supervised regime, when combined with keypoint annotations.
Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap
Authors: Tae Ha Park, Simone D'Amico
Date: 2022-03-08 00:00:00
Tasks: Multi-Task Learning, Pose Estimation, Spacecraft Pose Estimation

This work presents Spacecraft Pose Network v2 (SPNv2), a Convolutional Neural Network (CNN) for pose estimation of noncooperative spacecraft across domain gap. SPNv2 is a multi-scale, multi-task CNN which consists of a shared multi-scale feature encoder and multiple prediction heads that perform different tasks on a shared feature output. These tasks are all related to detection and pose estimation of a target spacecraft from an image, such as prediction of pre-defined satellite keypoints, direct pose regression, and binary segmentation of the satellite foreground. It is shown that by jointly training on different yet related tasks with extensive data augmentations on synthetic images only, the shared encoder learns features that are common across image domains that have fundamentally different visual characteristics compared to synthetic images. This work also introduces Online Domain Refinement (ODR) which refines the parameters of the normalization layers of SPNv2 on the target domain images online at deployment. Specifically, ODR performs self-supervised entropy minimization of the predicted satellite foreground, thereby improving the CNN's performance on the target domain images without their pose labels and with minimal computational efforts. The GitHub repository for SPNv2 is available at https://github.com/tpark94/spnv2.
End-to-End Semi-Supervised Learning for Video Action Detection
Authors: Akash Kumar, Yogesh Singh Rawat
Date: 2022-03-08 00:00:00
Proceeding: CVPR 2022 1
Tasks: Action Detection, Classification Consistency, Semantic Segmentation, Semi-Supervised Video Action Detection, Temporal Localization, Video Action Detection, Video Object Segmentation, Video Semantic Segmentation

In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data. We propose a simple end-to-end consistency based approach which effectively utilizes the unlabeled data. Video action detection requires both, action class prediction as well as a spatio-temporal localization of actions. Therefore, we investigate two types of constraints, classification consistency, and spatio-temporal consistency. The presence of predominant background and static regions in a video makes it challenging to utilize spatio-temporal consistency for action detection. To address this, we propose two novel regularization constraints for spatio-temporal consistency; 1) temporal coherency, and 2) gradient smoothness. Both these aspects exploit the temporal continuity of action in videos and are found to be effective for utilizing unlabeled videos for action detection. We demonstrate the effectiveness of the proposed approach on two different action detection benchmark datasets, UCF101-24 and JHMDB-21. In addition, we also show the effectiveness of the proposed approach for video object segmentation on the Youtube-VOS which demonstrates its generalization capability The proposed approach achieves competitive performance by using merely 20% of annotations on UCF101-24 when compared with recent fully supervised methods. On UCF101-24, it improves the score by +8.9% and +11% at 0.5 f-mAP and v-mAP respectively, compared to supervised approach.
A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds
Authors: Yan Xia, Qiangqiang Wu, Wei Li, Antoni B. Chan, Uwe Stilla
Date: 2022-03-08 00:00:00
Tasks: 3D Single Object Tracking, motion prediction, Object Tracking

Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we address this issue by explicitly leveraging temporal motion cues and propose DMT, a Detector-free Motion-prediction-based 3D Tracking network that completely removes the usage of complicated 3D detectors and is lighter, faster, and more accurate than previous trackers. Specifically, the motion prediction module is first introduced to estimate a potential target center of the current frame in a point-cloud-free manner. Then, an explicit voting module is proposed to directly regress the 3D box from the estimated target center. Extensive experiments on KITTI and NuScenes datasets demonstrate that our DMT can still achieve better performance (~10% improvement over the NuScenes dataset) and a faster tracking speed (i.e., 72 FPS) than state-of-the-art approaches without applying any complicated 3D detectors. Our code is released at \url{https://github.com/jimmy-dq/DMT}