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End-to-End High-Risk Tackle Detection System for Rugby

End-to-End High-Risk Tackle Detection System for Rugby

引用

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

作者： Nonaka, Naoki Fujihira, Ryo Nishio, Monami Murakami, Hidetaka Tajima, Takuya Yamada, Mutsuo Maeda, Akira Seita, Jun RIKEN Informat R&D & Strategy Headquarters Adv Data Sci Project Tokyo Japan Murakami Surg Hosp Nagasaki Japan Univ Miyazaki Fac Med Miyazaki Japan Ryutsu Keizai Univ Fac Hlth & Sport Sci Ryugasaki Japan Hakata Knee & Sports Clin Fukuoka Japan Kurume Univ Fac Human Hlth Kurume Fukuoka Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Reducing risk of severe injury such as concussion is a high priority for any contact sports. In rugby, Head Injury Assessment (HIA) protocol has been introduced to identify and protect players showing symptoms of concussion and having potential risk of concussion. However, on-field decisions by officials are sometimes difficult and subjective, and HIA is affordable only for elite leagues since it requires medical specialists. To make rugby matches more safe, we aim to develop a system to detect high-risk tackles, potential triggers of concussion, based on deep learning models. Our system takes rugby match video, then first identifies frame with tackle, subsequently detects location of tackle and estimate pose of the ball carrier and the tackler, and finally evaluate the risk of tackle using posture pair of players. Among the model combinations we have examined, the best performance was achieved with the combination of ResNet (2+1)D as tackle frame selection model, RetinaNet as tackle detection model and CenterTrack as pose estimation model. Evaluation using test data, a set of short clips from broadcasted rugby match videos, showed our system was able to detect 50% of high-risk tackles without any human intervention. This result opens a path for automated systems to detect high-risk events, leading to less expensive and more objective monitoring not only for rugby but also for any contact sports.

关键词： Deep learning computer vision Protocols Head conferences Pose estimation pattern recognition

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Slimmable Dataset Condensation

Slimmable Dataset Condensation

引用

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

作者： Liu, Songhua Ye, Jingwen Yu, Runpeng Wang, Xinchao Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350301298

Dataset distillation, also known as dataset condensation, aims to compress a large dataset into a compact synthetic one. Existing methods perform dataset condensation by assuming a fixed storage or transmission budget. When the budget changes, however, they have to repeat the synthesizing process with access to original datasets, which is highly cumbersome if not infeasible at all. In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results. We first study the limitations of existing dataset condensation algorithms on such a successive compression setting and identify two key factors: (1) the inconsistency of neural networks over different compression times and (2) the underdetermined solution space for synthetic data. Accordingly, we propose a novel training objective for slimmable dataset condensation to explicitly account for both factors. Moreover, synthetic datasets in our method adopt a significance-aware parameterization. Theoretical derivation indicates that an upper-bounded error can be achieved by discarding the minor components without training. Alternatively, if training is allowed, this strategy can serve as a strong initialization that enables a fast convergence. Extensive comparisons and ablations demonstrate the superiority of the proposed solution over existing methods on multiple benchmarks.

关键词： Efficient and scalable vision

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HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering

HyperCUT: Video Sequence from a Single Blurry Image using Un...

引用

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

作者： Bang-Dang Pham Phong Tran Anh Tran Cuong Pham Rang Nguyen Minh Hoai VinAI Res Hanoi Vietnam MBZUAI Abu Dhabi U Arab Emirates Posts & Telecommun Inst Tech Ho Chi Minh City Vietnam SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9798350301298

We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input. A critical issue disturbing the training of an image-to-video model is the ambiguity of the frame ordering since both the forward and backward sequences are plausible solutions. This paper proposes an effective self-supervised ordering scheme that allows training high-quality image-to-video deblurring models. Unlike previous methods that rely on order-invariant losses, we assign an explicit order for each video sequence, thus avoiding the order-ambiguity issue. Specifically, we map each video sequence to a vector in a latent high-dimensional space so that there exists a hyperplane such that for every video sequence, the vectors extracted from it and its reversed sequence are on different sides of the hyperplane. The side of the vectors will be used to define the order of the corresponding sequence. Last but not least, we propose a real-image dataset for the image-to-video deblurring problem that covers a variety of popular domains, including face, hand, and street. Extensive experimental results confirm the effectiveness of our method. Code and data are available at https://***/VinAIResearch/ ***

关键词： Low-level vision

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Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning

Temporal Attention Unit: Towards Efficient Spatiotemporal Pr...

引用

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

作者： Tan, Cheng Gao, Zhangyang Wu, Lirong Xu, Yongjie Xia, Jun Li, Siyuan Li, Stan Z. Westlake Univ AI Lab Res Ctr Ind Future Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Spatiotemporal predictive learning aims to generate future frames by learning from historical frames. In this paper, we investigate existing methods and present a general framework of spatiotemporal predictive learning, in which the spatial encoder and decoder capture intra-frame features and the middle temporal module catches inter-frame correlations. While the mainstream methods employ recurrent units to capture long-term temporal dependencies, they suffer from low computational efficiency due to their unparallelizable architectures. To parallelize the temporal module, we propose the Temporal Attention Unit (TAU), which decomposes temporal attention into intra-frame statical attention and inter-frame dynamical attention. Moreover, while the mean squared error loss focuses on intra-frame errors, we introduce a novel differential divergence regularization to take inter-frame variations into account. Extensive experiments demonstrate that the proposed method enables the derived model to achieve competitive performance on various spatiotemporal prediction benchmarks.

关键词： Low-level vision

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Learning to Render Novel Views from Wide-Baseline Stereo Pairs

Learning to Render Novel Views from Wide-Baseline Stereo Pai...

引用

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

作者： Du, Yilun Smith, Cameron Tewari, Ayush Sitzmann, Vincent MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and due to the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. We demonstrate that our method learns powerful multi-view geometry priors while reducing the rendering time. We conduct extensive comparisons on held-out test scenes across two real-world datasets, significantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis.

关键词： vision + graphics

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DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

DANI-Net: Uncalibrated Photometric Stereo by Differentiable ...

引用

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

作者： Li, Zongrui Zheng, Qian Shi, Boxin Pan, Gang Jiang, Xudong Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Zhejiang Univ State Key Lab Brain Machine Intelligence Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol I Iangzhou Peoples R China Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance.

关键词： Physics-based vision and shape-from-X

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Where are they looking in the 3D space?

Where are they looking in the 3D space?

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Horanyi, Nora Zheng, Linfang Chong, Eunji Leonardis, Aleš Chang, Hyung Jin University of Birmingham School of Computer Science United Kingdom *** Inc.

ISBN: (纸本)9798350302493

We propose a novel depth-aware joint attention target estimation framework that estimates the attention target in 3D space. Our goal is to mimic human's ability to understand where each person is looking in their proximity. In this work, we tackle the previously unexplored problem of utilising a depth prior along with a 3D joint FOV probability map to estimate the joint attention target of people in the scene. We leverage the insight that besides the 2D image content, strong gaze-related constraints exist in the depth order of the scene and different subject-specific attributes. Extensive experiments show that our method outperforms favourably against existing joint attention target estimation methods on the VideoCoAtt benchmark dataset. Despite the proposed framework being designed for joint attention target estimation, we show that it outperforms single attention target estimation methods on both the GazeFollow image and the VideoAttentionTarget video benchmark datasets. © 2023 ieee.

关键词： computer vision

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Complexity-guided Slimmable Decoder for Efficient Deep Video Compression

Complexity-guided Slimmable Decoder for Efficient Deep Video...

引用

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

作者： Hu, Zhihao Xu, Dong Beihang Univ Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression. Specifically, given the target complexity constraints, in our cgSlimDecoder, we introduce a set of new channel width selection modules to automatically decide the optimal channel width of each slimmable convolution layer. By optimizing the complexity-rate-distortion related objective function to directly learn the parameters of the newly introduced channel width selection modules and other modules in the decoder, our cgSlimDecoder can automatically allocate the optimal numbers of parameters for different types of modules (e.g., motion/residual decoder and the motion compensation network) and simultaneously support multiple complexity levels by using a single learnt decoder instead of multiple decoders. In addition, our proposed SaEC can further accelerate the entropy decoding procedure in both motion and residual decoders by simply skipping the entropy coding process for the elements in the encoded feature maps that are already well-predicted by the hyperprior network. As demonstrated in our comprehensive experiments, our newly proposed methods cgSlimDecoder and SaEC are general and can be readily incorporated into three widely used deep video codecs (i.e., DVC, FVC and DCVC) to significantly improve their coding efficiency with negligible performance drop.

关键词： Low-level vision

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Learning Sample Relationship for Exposure Correction

Learning Sample Relationship for Exposure Correction

引用

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

作者： Huang, Jie Zhao, Feng Zhou, Man Xiao, Jie Zheng, Naishan Zheng, Kaiwen Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency. In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a mini-batch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design.

关键词： Low-level vision

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Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval

Pic2Word: Mapping Pictures to Words for Zero-shot Composed I...

引用

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

作者： Saito, Kuniaki Sohn, Kihyuk Zhang, Xiang Li, Chun-Liang Lee, Chen-Yu Saenko, Kate Pfister, Tomas Boston Univ Boston MA 02215 USA Google Cloud AI Res Mountain View CA 94043 USA Google Res Mountain View CA USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. Labeling such triplets is expensive and hinders broad applicability of CIR. In this work, we propose to study an important task, Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training. To this end, we propose a novel method, called Pic2Word, that requires only weakly labeled image-caption pairs and unlabeled image datasets to train. Unlike existing supervised CIR models, our model trained on weakly labeled or unlabeled datasets shows strong generalization across diverse ZS-CIR tasks, e.g., attribute editing, object composition, and domain conversion. Our approach outperforms several supervised CIR methods on the common CIR benchmark, CIRR and Fashion-IQ. Code will be made publicly available at https://***/google-research/composed_image_retrieval

关键词： language reasoning vision

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