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Multi-Modal Hit Detection and Positional Analysis in Padel Competitions

Multi-Modal Hit Detection and Positional Analysis in Padel C...

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

作者： Decorte, Robbe Pare, Martin Vanhaeverbeke, Jelle Taelman, Joachim Slembrouck, Maarten Verstockt, Steven Univ Ghent imec IDLab Ghent Belgium

ISBN: (纸本)9798350365474

Padel is a rapidly growing racquet sport and has gained popularity globally due to its accessibility and exciting gameplay dynamics. Effective coordination between teammates hinges on maintaining an appropriate distance, allowing for seamless transitions between offensive and defensive maneuvers. A balanced inter-player distance and distance to the net not only facilitates efficient communication but also enhances the team's ability to exploit openings in the opponent's defense while minimizing vulnerabilities. We introduce a new open dataset of padel rallies with annotations for hits and player-ball interactions, a predictive model for detecting hits based on audio signals, a reidentification algorithm for pose tracking, and a framework for calculating inter-player and player-net distances during rallies. Our predictive model achieves an average F1-score of 92% for hit detection, demonstrating robust performance across different match conditions. Furthermore, we develop a system for accurately assigning hits to individual players, achieving an overall accuracy of 83.70% for player-specific assignment and 86.83% for team-based assignment.

关键词： computer vision Deep Learning Player Tracking Sound Event Detection Sports Data Science

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Seeing the World in a Bag of Chips

Seeing the World in a Bag of Chips

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

作者： Park, Jeong Joon Holynski, Aleksander Seitz, Steven M. Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781728171685

We address the dual problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors. Our contributions include 1) modeling highly specular objects, 2) modeling inter-reflections and Fresnel effects, and 3) enabling surface light field reconstruction with the same input needed to reconstruct shape alone. In cases where scene surface has a strong mirror-like material component, we generate highly detailed environment images, revealing room composition, objects, people, buildings, and trees visible through windows. Our approach yields state of the art view synthesis techniques, operates on low dynamic range imagery, and is robust to geometric and calibration errors.

关键词： computer vision

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Can We Characterize Tasks Without Labels or Features?

Can We Characterize Tasks Without Labels or Features?

引用

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

作者： Wallace, Bram Wu, Ziyang Hariharan, Bharath Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task. Methods, however, generally depend on two separate assumptions: the presence of labeled images and access to powerful "probe" networks that yield useful features. In this work, we demonstrate the current reliance on both of these aspects and develop algorithms to operate when either of these assumptions fail. In the unlabeled case, we show that pseudolabels from the probe network provide discriminative enough gradients to perform nearly-equal task selection even when the probe network is trained on imagery unrelated to the tasks. To compute the embedding with no probe network at all, we introduce the Task Tangent Kernel (TTK) which uses a kernelized distance across multiple random networks to achieve performance over double that of other methods with randomly initialized models.

关键词： computer vision Codes Shape Computational modeling Natural languages pattern recognition Task analysis

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SCENIC: A JAX Library for computer vision Research and Beyond

SCENIC: A JAX Library for Computer Vision Research and Beyon...

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

作者： Dehghani, Mostafa Gritsenko, Alexey Arnab, Anurag Minderer, Matthias Tay, Yi Google Brain Mountain View CA 94043 USA Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

SCENIC is an open-source' JAX library with a focus on transformer-based models for computer vision research and beyond. The goal of this toolkit is to facilitate rapid experimentation, prototyping, and research of new architectures and models. SCENIC supports a diverse range of tasks (e.g., classification, segmentation, detection) and facilitates working on multi-modal problems, along with GPU/TPU support for large-scale, multi-host and multi-device training. SCENIC also offers optimized implementations of stateof-the-art research models spanning a wide range of modalities. SCENIC has been successfully used for numerous projects and published papers and continues serving as the library of choice for rapid prototyping and publication of new research ideas.

关键词： Training computer vision Computational modeling Machine learning Transformers Rapid prototyping Libraries

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Learning to Name Classes for vision and Language Models

Learning to Name Classes for Vision and Language Models

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

作者： Parisot, Sarah Yang, Yongxin McDonagh, Steven Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.

关键词： language reasoning vision

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Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge

Teeth-SEG: An Efficient Instance Segmentation Framework for ...

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

作者： Zou, Bo Wang, Shaofeng Liu, Hao Sun, Gaoyue Wang, Yajie Zuo, FeiFei Quan, Chengbin Zhaot, Youjian Tsinghua Univ Beijing Peoples R China Capital Med Univ Beijing Peoples R China Imperial Coll London London England LargeV Inc Beijing Peoples R China Tsinghua Univ Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Teeth localization, segmentation, and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes ( e.g., maxillary first premolar and second premolar), 2) the teeth's position and shape variation across subjects, and 3) the presence of abnormalities in the dentition (e.g., caries and edentulism). To address these problems, we propose a ViT-based framework named TeethSEG, which consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer. Specifically, to compose the two modules, we design a unique permutation-based upscaler to ensure high efficiency while establishing clear segmentation boundaries with multi-head self/cross-gating layers to emphasize particular semantics meanwhile maintaining the divergence between token embeddings. Besides, we collect the first open-sourced intraoral image dataset IO150K, which comprises over 150k intraoral photos, and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental image segmentation.

关键词： Image Segmentation Intra-Oral Image Medical vision Teeth Segmentation

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Towards Language-Driven Video Inpainting via Multimodal Large Language Models

Towards Language-Driven Video Inpainting via Multimodal Larg...

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

作者： Wu, Jianzong Li, Xiangtai Si, Chenyang Zhou, Shangchen Yang, Jingkang Zhang, Jiangning Li, Yining Chen, Kai Tong, Yunhai Liu, Ziwei Loy, Chen Change Peking Univ Natl Key Lab Gen Artificial Intelligence Beijing Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Shanghai AI Lab Shanghai Peoples R China PKU Wuhan Inst Artificial Intelligence Wuhan Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350353006

We introduce a new task - language-driven video inpainting, which uses natural language instructions to guide the inpainting process. This approach overcomes the limitations of traditional video inpainting methods that de-pend on manually labeled binary masks, a process often tedious and labor-intensive. We present the Remove Objects from Videos by Instructions (ROVI) dataset, containing 5,650 videos and 9,091 inpainting results, to support training and evaluation for this task. We also propose a novel diffusion-based language-driven video inpainting framework, the first end-to-end baseline for this task, integrating Multimodal Large Language Models to understand and execute complex language-based inpainting requests effectively. Our comprehensive results showcase the dataset's versatility and the model's effectiveness in various language-instructed inpainting scenarios. We have made datasets, code, and models publicly available at https://***/jianzongwu/Language-Driven-Video-Inpainting.

关键词： computer vision Multimodal Learning Text-to-Video Generation Video Inpainting

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Zero-Shot Audio-Visual Compound Expression recognition Method based on Emotion Probability Fusion

Zero-Shot Audio-Visual Compound Expression Recognition Metho...

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

作者： Ryumina, Elena Markitantov, Maxim Ryumin, Dmitry Kaya, Heysem Karpov, Alexey Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg Russia Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands

ISBN: (纸本)9798350365474

A Compound Expression recognition (CER) as a sub-field of affective computing is a novel task in intelligent human-computer interaction and multimodal user interfaces. We propose a novel audio-visual method for CER. Our method relies on emotion recognition models that fuse modalities at the emotion probability level, while decisions regarding the prediction of compound expressions are based on the pair-wise sum of weighted emotion probability distributions. Notably, our method does not use any training data specific to the target task. Thus, the problem is a zero-shot classification task. The method is evaluated in multi-corpus training and cross-corpus validation setups. We achieved F1 scores of 32.15% and 25.56% for the AffWild2 and C-EXPR-DB test subsets without training on target corpus and target task, respectively. Therefore, our method is on par with methods developed training target corpus or target task. The source code is publicly available from https: //***/AVCER/.

关键词： audio-visual emotion recognition compound expression recognition zero-shot classification

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Three Pillars improving vision Foundation Model Distillation for Lidar

Three Pillars improving Vision Foundation Model Distillation...

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

作者： Puy, Gilles Gidaris, Spyros Boulch, Alexandre Simeoni, Oriane Sautier, Corentin Perez, Patrick Bursucl, Andrei Marlet, Renaud Valeo ai Paris France Kyutai Paris France Univ Gustave Eiffel CNRS LIGM Ecole Ponts Marne La Vallee France

ISBN: (纸本)9798350353006

Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervision. Ideally, 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results, obtained thanks to distillation methods that keep improving. Yet, we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work, instead of focusing only on the distillation method, we study the effect of three pillars for distillation: the 3D backbone, the pretrained 2D backbone, and the pretraining 2D+3D dataset. In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features, and to improve the robustness of the pretrained backbones to domain gaps and perturbations. The code is available at https://***/valeoai/ScaLR.

关键词： Semantic Segmentation

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LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion

LAKE-RED: Camouflaged Images Generation by Latent Background...

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

作者： Zhao, Pancheng Xu, Peng Qin, Pengda Fan, Deng-Ping Zhang, Zhicheng Jia, Guoli Zhou, Bowen Yang, Jufeng Nankai Univ Coll Comp Sci VCIP Tianjin Peoples R China Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China Nankai Univ Coll Comp Sci DISSec Tianjin Peoples R China Nankai Int Adv Res Inst Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs, this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However, the existing camouflaged generation methods require specifying the background manually, thus failing to extend the camouflaged sample diversity in a low-cost manner. In this paper, we propose a Latent Background Knowledge Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. To our knowledge, our contributions mainly include: (1) For the first time, we propose a camouflaged generation paradigm that does not need to receive any background inputs. (2) Our LAKE-RED is the first knowledge retrieval-augmented method with interpretability for camouflaged generation, in which we propose an idea that knowledge retrieval and reasoning enhancement are separated explicitly, to alleviate the task-specific challenges. Moreover, our method is not restricted to specific foreground targets or backgrounds, offering a potential for extending camouflaged vision perception to more diverse domains. (3) Experimental results demonstrate that our method outperforms the existing approaches, generating more realistic camouflage images. Our source code is released on https://***/PanchengZhao/LAKE-RED.

关键词： camouflage object detection camouflaged vision perception diffusion generative model knowledge retrieval reasoning enhancement

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