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共 4655 条记录，以下是61-70 订阅

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Rugby Scene Classification Enhanced by vision Language Model

Rugby Scene Classification Enhanced by Vision Language Model

引用

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

作者： Nonaka, Naoki Fujihira, Ryo Koshiba, Toshiki Maeda, Akira Seita, Jun RIKEN Informat R&D & Strategy Headquarters Adv Data Sci Project Wako Saitama Japan Hakata Knee & Sports Clin Fukuoka Japan

ISBN: (纸本)9798350365474

This study investigates the integration of vision language models (VLM) to enhance the classification of situations within rugby match broadcasts. The importance of accurately identifying situations in sports videos is emphasized for understanding game dynamics and facilitating downstream tasks like performance evaluation and injury prevention. Utilizing a dataset comprising 18, 000 labeled images extracted at 0.2-second intervals from 100 minutes of rugby match broadcasts, scene classification tasks including contact plays (scrums, mauls, rucks, tackles, lineouts), rucks, tackles, lineouts, and multiclass classification were performed. The study aims to validate the utility of VLM outputs in improving classification performance compared to using solely image data. Experimental results demonstrate substantial performance improvements across all tasks with the incorporation of VLM outputs. Our analysis of prompts suggests that, when provided with appropriate contextual information through natural language, VLMs can effectively capture the context of a given image. The findings of our study indicate that leveraging VLMs in the domain of sports analysis holds promise for developing image processing models capable of incorpolating the tacit knowledge encoded within language models, as well as information conveyed through natural language descriptions.

关键词： Rugby Scene classification vision language model

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Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large vision Language Model

Divide and Conquer Boosting for Enhanced Traffic Safety Desc...

引用

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

作者： Khai Trinh Xuan Khoi Nguyen Nguyen Bach Hoang Ngo Vu Dinh Xuan Minh-Hung An Quang-Vinh Dinh Ho Chi Minh City Univ Technol VNU HCM Ho Chi Minh City Vietnam Univ Sci VNU HCM Ho Chi Minh City Vietnam Univ Informat Technol VNU HCM Ho Chi Minh City Vietnam FPT Telecom Hanoi Vietnam AI Lab AI VIETNAM Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam

ISBN: (纸本)9798350365474

The increasing complexity of traffic dynamics has underscored the necessity for advanced traffic safety description and analysis, challenging the efficacy of current methodologies in comprehensively understanding and predicting safety conditions from transportation videos. This paper addresses these challenges by analyzing specific segments crucial for precise traffic safety descriptions. Through this examination, we introduce an innovative preprocessing method named "segment extraction", facilitating the creation of a novel segment-based training dataset. Additionally, we present a practical two-stage training framework specifically tailored for this dataset. This framework is designed to produce accurate descriptions of traffic safety by incorporating the unique attributes of our segment-based training datasets. Leveraging these contributions, our method achieved a notable 2nd rank with a score of 32.8877 in the AI City Challenge Track2 test set: Traffic Safety Description and Analysis 2024. The source code for the proposed approaches is openly accessible at https://***/AIVIETNAMResearch/AI-CIty2024-Track2

关键词： large language model large vision language model video captioning video instance captioning video-text model

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CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

引用

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

作者： Senadeera, Damith Chamalke Yang, Xiaoyun Kollias, Dimitrios Slabaugh, Gregory Queen Mary Univ London Sch Elect Engn & Comp Sci London England Queen Marys Digital Environm Res Inst DERI London England Remark AI UK Ltd London England

ISBN: (纸本)9798350365474

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatio-temporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at (1).

关键词： computer vision Cropping Deep Learning Efficient Additive Attention UniFormerV2 Video Analytics Violence Detection

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On Visible Adversarial Perturbations & Digital Watermarking 31

On Visible Adversarial Perturbations & Digital Watermarking

引用

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

作者： Hayes, Jamie UCL London England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Given a machine learning model, adversarial perturbations transform images such that the model's output is classified as an attacker chosen class. Most research in this area has focused on adversarial perturbations that are imperceptible to the human eye. However, recent work has considered attacks that are perceptible but localized to a small region of the image. Under this threat model, we discuss both defenses that remove such adversarial perturbations, and attacks that can bypass these defenses.

关键词： Perturbation methods Watermarking Computational modeling Visualization Task analysis Image restoration computer vision

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Pseudo-label based unsupervised fine-tuning of a monocular 3D pose estimation model for sports motions

Pseudo-label based unsupervised fine-tuning of a monocular 3...

引用

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

作者： Suzuki, Tomohiro Tanaka, Ryota Takeda, Kazuya Fujii, Keisuke Nagoya Univ Nagoya Aichi Japan

ISBN: (纸本)9798350365474

Accurate motion capture is useful for sports motion analysis, but requires higher acquisition costs. Monocular or few camera multi-view pose estimation provides an accessible but less accurate alternative, especially for sports motion, due to training on datasets of daily activities. In addition, multi-view estimation is still costly due to camera calibration. Therefore, it is desirable to develop an accurate and cost-effective motion capture system for the daily training in sports. In this paper, we propose an accurate and convenient sports motion capture system based on unsupervised fine-tuning. The proposed system estimates 3D joint positions by multi-view estimation based on automatic calibration with the human body. These results are used as pseudo-labels for fine-tuning of the recent higher performance monocular 3D pose estimation model. Since the fine-tuning improves the model accuracy for sports motion, we can choose multi-view or monocular estimation depending on the situation. We evaluated the system using a running motion dataset and ASPset-510, and showed that fine-tuning improved the performance of monocular estimation to the same level as that of multi-view estimation for running motion. Our proposed system can be useful for the daily motion analysis in sports.

关键词： computer vision Pose estimation Running Sports

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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

引用

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

作者： Hartley, Zane K. J. Lind, Rob J. Pound, Michael P. French, Andrew P. Univ Nottingham Wollaton Rd Nottingham NG8 1BB England Syngenta Jealotts Hill Int Res Ctr Warfield England

ISBN: (纸本)9798350365474

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Agriculture computer vision ControlNet Deep Learning Diffusion LoRA Plant Phenotyping

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Designing a Moral Compass for the Future of computer vision using Speculative Analysis 30

Designing a Moral Compass for the Future of Computer Vision ...

引用

30th ieee/cvf conference on computer vision and pattern recognition workshops (cvprw)

作者： Skirpan, Michael Yeh, Tom Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9781538607336

In this paper we discuss and analyze possible futures for technologies in the field of computer vision (CV). Using a method we have coined speculative analysis we take a broad look at research trends in the field to categorize risks, analyze which ones are most threatening and likely, and ultimately summarize conclusions for how the field may attempt to stem future harms caused by CV technologies. We develop narrative case studies to provoke dialogue and deeply explore possible risk scenarios we found to be most probable and severe. We arrive at the position that there are serious potentials for CV to cause discriminatory harm and exacerbate cybersecurity issues.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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De-noised vision-language Fusion Guided by Visual Cues for E-commerce Product Search

De-noised Vision-language Fusion Guided by Visual Cues for E...

引用

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

作者： Hu, Zhizhang Li, Shasha Du, Ming Dhua, Arnab Gray, Douglas Univ Calif Merced Merced CA 95343 USA Amazon Visual Search & AR Seattle WA USA Amazon Seattle WA USA

ISBN: (纸本)9798350365474

In e-commerce applications, vision-language multimodal transformer models play a pivotal role in product search. The key to successfully training a multimodal model lies in the alignment quality of image-text pairs in the dataset. However, the data in practice is often automatically collected with minimal manual intervention. Hence the alignment of image-text pairs is far from ideal. In e-commerce, this misalignment can stem from noisy and redundant non-visual-descriptive text attributes in the product description. To address this, we introduce the MultiModal alignment-guided Learned Token Pruning (MM-LTP) method. MM-LTP employs token pruning, conventionally used for computational efficiency, to perform online text cleaning during multimodal model training. By enabling the model to discern and discard unimportant tokens, it is able to train with implicitly cleaned image-text pairs. We evaluate MM-LTP using a benchmark multimodal e-commerce dataset comprising over 710,000 unique Amazon products. Our evaluation hinges on visual search, a prevalent e-commerce feature. Through MM-LTP, we demonstrate that refining text tokens enhances the paired image branch's training, which leads to significantly improved visual search performance.

关键词： De-noised Fusion Multimodal Learning Token Pruning vision-language Model Visual Search

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

引用

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

作者： Simpson, Benjamin Lubana, Ekdeep Liu, Yuchen Dick, Robert Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781728193601

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Image enhancement

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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

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

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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