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Detector-Free Weakly Supervised Group Activity recognition

Detector-Free Weakly Supervised Group Activity Recognition

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kim, Dongkeun Lee, Jinsung Cho, Minsu Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Group activity recognition is the task of understanding the activity conducted by a group of people as a whole in a multi-person video. Existing models for this task are often impractical in that they demand ground-truth bounding box labels of actors even in testing or rely on off-the-shelf object detectors. Motivated by this, we propose a novel model for group activity recognition that depends neither on bounding box labels nor on object detector. Our model based on Transformer localizes and encodes partial contexts of a group activity by leveraging the attention mechanism, and represents a video clip as a set of partial context embeddings. The embedding vectors are then aggregated to form a single group representation that reflects the entire context of an activity while capturing temporal evolution of each partial context. Our method achieves outstanding performance on two benchmarks, Volleyball and NBA datasets, surpassing not only the state of the art trained with the same level of supervision, but also some of existing models relying on stronger supervision.

关键词： computer vision Image analysis Computational modeling Supervised learning Detectors Activity recognition Benchmark testing

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Boosting Co-teaching with Compression Regularization for Label Noise

Boosting Co-teaching with Compression Regularization for Lab...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Yingyi Shen, Xi Hu, Shell Xu Suykens, Johan A. K. Katholieke Univ Leuven ESAT STADIUS Leuven Belgium UPE Ecole Ponts LIGM UMR 8049 Champs Sur Marne France Upload AI LLC Houston TX USA

ISBN: (纸本)9781665448994

In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout [22]. We find that Nested Dropout [22], though originally proposed to perform fast information retrieval and adaptive data compression, can properly regularize a neural network to combat label noise. Moreover, owing to its simplicity, it can be easily combined with Co-teaching [5] to further boost the performance. Our final model remains simple yet effective: it achieves comparable or even better performance than the state-of-the-art approaches on two real-world datasets with label noise which are Clothing1M [28] and ANIMAL-10N [24]. On Clothing1M [28], our approach obtains 74.9% accuracy which is slightly better than that of DivideMix [12]. On ANIMAL-10N [24], we achieve 84.1% accuracy while the best public result by PLC [30] is 83.4%. We hope that our simple approach can be served as a strong baseline for learning with label noise. Our implementation is available at https://***/yingyichen-cyy/Nested-Co-teaching.

关键词： Adaptation models computer vision Image coding conferences Neural networks Data compression Information retrieval

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Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement

Overparametrization of HyperNetworks at Fixed FLOP-Count Ena...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Muller, Lorenz K. Huawei Technol Zurich Res Ctr Zurich Switzerland

ISBN: (纸本)9781665448994

Deep convolutional neural networks can enhance images taken with small mobile camera sensors and excel at tasks like demoisaicing, denoising and super-resolution. However, for practical use on mobile devices these networks often require too many FLOPs and reducing the FLOPs of a convolution layer, also reduces its parameter count. This is problematic in view of the recent finding that heavily over-parameterized neural networks are often the ones that generalize best. In this paper we propose to use HyperNetworks to break the fixed ratio of FLOPs to parameters of standard convolutions. This allows us to exceed previous state-of-the-art architectures in SSIM and MS-SSIM on the Zurich RAWto-DSLR (ZRR) data-set at > 10x reduced FLOP-count. On ZRR we further observe generalization curves consistent with 'double-descent' behavior at fixed FLOP-count, in the large image limit. Finally we demonstrate the same technique can be applied to an existing network (VDN) to reduce its computational cost while maintaining fidelity on the Smartphone Image Denoising Dataset (SIDD). Code for key functions is given in the supplemental.

关键词： Training Convolution Noise reduction computer architecture Cameras Energy efficiency Sensors

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Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting

Camera Calibration and Player Localization in SoccerNet-v2 a...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cioppa, Anthony Deliege, Adrien Magera, Floriane Giancola, Silvio Barnich, Olivier Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Liege Belgium EVS Broadcast Equipment Seraing Belgium KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665448994

Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games. We further release our distilled network, and leverage it to provide 3 ways of representing the calibration results along with player localization. Finally, we exploit those representations within the current best architecture for the action spotting task of SoccerNet-v2, and achieve new state-of-the-art performances.

关键词： Location awareness Deep learning computer vision Games computer architecture Tools Cameras

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Micro-Expression Classification based on Landmark Relations with Graph Attention Convolutional Network

Micro-Expression Classification based on Landmark Relations ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kumar, Ankith Jain Rakesh Bhanu, Bir Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (纸本)9781665448994

Facial micro-expressions are brief, rapid, spontaneous gestures of the facial muscles that express an individual's genuine emotions. Because of their short duration and subtlety, detecting and classifying these micro-expressions by humans and machines is difficult. In this paper, a novel approach is proposed that exploits relationships between landmark points and the optical flow patch for the given landmark points. It consists of a two-stream graph attention convolutional network that extracts the relationships between the landmark points and local texture using an optical flow patch. A graph structure is built to draw-out temporal information using the triplet of frames. One stream is for node feature location, and the other one is for a patch of optical-flow information. These two streams (node location stream and optical flow stream) are fused for classification. The results are shown on, CASME II and SAMM, publicly available datasets, for three classes and five classes of micro-expressions. The proposed approach outperforms the state-of-the-art methods for 3 and 5 categories of expressions.

关键词： computer vision conferences Feature extraction Facial muscles pattern recognition Optical flow

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Editing like Humans: A Contextual, Multimodal Framework for Automated Video Editing

Editing like Humans: A Contextual, Multimodal Framework for ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Koorathota, Sharath Adelman, Patrick Cotton, Kelly Sajda, Paul Columbia Univ Dept Biomed Engn New York NY 10027 USA Fovea Inc New York NY 10001 USA CUNY Grad Ctr Dept Psychol New York NY USA

ISBN: (纸本)9781665448994

We propose an automated video editing model, which we term contextual and multimodal video editing (CMVE). The model leverages visual and textual metadata describing videos, integrating essential information from both modalities, and uses a learned editing style from a single example video to coherently combine clips. The editing model is useful for tasks such as generating news clip montages and highlight reels given a text query that describes the video storyline. The model exploits the perceptual similarity between video frames, objects in videos and text descriptions to emulate coherent video editing. Amazon Mechanical Turk participants made judgements comparing CMVE to expert human editing. Experimental results showed no significant difference in the CMVE vs human edited video in terms of matching the text query and the level of interest each generates, suggesting CMVE is able to effectively integrate semantic information across visual and textual modalities and create perceptually coherent quality videos typical of human video editors. We publicly release an online demonstration of our method.

关键词： Training Visualization computer vision conferences Semantics Metadata pattern recognition

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Learning to Solve Hard Minimal Problems

Learning to Solve Hard Minimal Problems

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hruby, Petr Duff, Timothy Leykin, Anton Pajdla, Tomas Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Univ Washington Dept Math Seattle WA 98195 USA Georgia Inst Technol Sch Math Atlanta GA 30332 USA Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present an approach to solving hard geometric optimization problems in the RANSAC framework. The hard minimal problems arise from relaxing the original geometric optimization problem into a minimal problem with many spurious solutions. Our approach avoids computing large numbers of spurious solutions. We design a learning strategy for selecting a starting problem-solution pair that can be numerically continued to the problem and the solution of interest. We demonstrate our approach by developing a RANSAC solver for the problem of computing the relative pose of three calibrated cameras, via a minimal relaxation using four points in each view. On average, we can solve a single problem in under 70 mu s. We also benchmark and study our engineering choices on the very familiar problem of computing the relative pose of two calibrated cameras, via the minimal case of five points in two views.

关键词： computer vision Three-dimensional displays Codes Machine learning Cameras Resource description framework Sensors

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Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation

Unsupervised Detection of Cancerous Regions in Histology Ima...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Stepec, Dejan Skocaj, Danijel Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia XLAB Doo Pot Za Brdom 100 Ljubljana 1000 Slovenia

ISBN: (纸本)9781665448994

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labeled data. Obtaining labeled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which often come in large diversity and complexity. Recently presented approaches for unsupervised detection of visual anomalies approaches omit the need for labeled data and demonstrate promising results in domains, where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a one-fits-all solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.

关键词： Visualization computer vision Image analysis Histopathology conferences Biomedical measurement pattern recognition

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Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices

Extremely Lightweight Quantization Robust Real-Time Single-I...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665448994

Single-Image Super Resolution (SISR) is a classical computer vision problem and it has been studied for over decades. With the recent success of deep learning methods, recent work on SISR focuses solutions with deep learning methodologies and achieves state-of-the-art results. However most of the state-of-the-art SISR methods contain millions of parameters and layers, which limits their practical applications. In this paper, we propose a hardware (Synaptics Dolphin NPU) limitation aware, extremely lightweight quantization robust real-time super resolution network (XLSR). The proposed model's building block is inspired from root modules introduced in [15] for Image classification. We successfully applied root modules to SISR problem, further more to make the model uint8 quantization robust we used Clipped ReLU at the last layer of the network and achieved great balance between reconstruction quality and runtime. Furthermore, although the proposed network contains 30x fewer parameters than VDSR [16] its performance surpasses it on Div2K validation set. The network proved itself by winning Mobile AI 2021 Real-Time Single Image Super Resolution Challenge.

关键词： Performance evaluation Deep learning computer vision Quantization (signal) Image resolution Runtime Computational modeling

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Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities

Assembly101: A Large-Scale Multi-View Video Dataset for Unde...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sener, Fadime Chatterjee, Dibyadip Shelepov, Daniel He, Kun Singhania, Dipika Wang, Robert Yao, Angela Meta Real Labs Res Pittsburgh PA 15222 USA Natl Univ Singapore Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instructions, and the sequences feature rich and natural variations in action ordering, mistakes, and corrections. Assembly101 is the first multi-view action dataset, with simultaneous static (8) and egocentric (4) recordings. Sequences are annotated with more than 100K coarse and IM fine-grained action segments, and 18M 3D hand poses. We benchmark on three action understanding tasks: recognition, anticipation and temporal segmentation. Additionally, we propose a novel task of detecting mistakes. The unique recording format and rich set of annotations allow us to investigate generalization to new toys, cross-view transfer, long-tailed distributions, and pose vs. appearance. We envision that Assembly101 will serve as a new challenge to investigate various activity understanding problems.

关键词： Training computer vision Three-dimensional displays Toy manufacturing industry Recording pattern recognition Synchronization

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