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A Perspective on Deep vision Performance with Standard Image and Video Codecs

A Perspective on Deep Vision Performance with Standard Image...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Christoph Reich Oliver Hahn Daniel Cremers Stefan Roth Biplob Debnath TU Darmstadt TU Munich NEC Laboratories America Inc. Munich Center for Machine Learning (MCML) Hessian Center for AI (hessian.AI)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Resource-constrained hardware, such as edge devices or cell phones, often rely on cloud servers to provide the required computational resources for inference in deep vision models. However, transferring image and video data from an edge or mobile device to a cloud server requires coding to deal with network constraints. The use of standardized codecs, such as JPEG or H.264, is prevalent and required to ensure interoperability. This paper aims to examine the implications of employing standardized codecs within deep vision pipelines. We find that using JPEG and H.264 coding significantly deteriorates the accuracy across a broad range of vision tasks and models. For instance, strong compression rates reduce semantic segmentation accuracy by more than 80% in mIoU. In contrast to previous findings, our analysis extends beyond image and action classification to localization and dense prediction tasks, thus providing a more comprehensive perspective.

关键词： Video coding Image coding Accuracy Computational modeling Semantic segmentation Transform coding Encoding

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Two-Person Interaction Augmentation with Skeleton Priors

Two-Person Interaction Augmentation with Skeleton Priors

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Baiyi Li Edmond S. L. Ho Hubert P. H. Shum He Wang University of Leeds United Kingdom University of Glasgow United Kingdom Durham University United Kingdom University College London United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Close and continuous interaction with rich contacts is a crucial aspect of human activities (e.g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc. However, acquiring such skeletal motion is challenging. While direct motion capture is expensive and slow, motion editing/generation is also non-trivial, as complex contact patterns with topological and geometric constraints have to be retained. To this end, we propose a new deep learning method for two-body skeletal interaction motion augmentation, which can generate variations of contact-rich interactions with varying body sizes and proportions while retaining the key geometric/topological relations between two bodies. Our system can learn effectively from a relatively small amount of data and generalize to drastically different skeleton sizes. Through exhaustive evaluation and comparison, we show it can generate high-quality motions, has strong generalizability and outperforms traditional optimization-based methods and alternative deep learning solutions.

关键词： Deep learning computer vision conferences Activity recognition Animation Skeleton Motion capture

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A visualization method for data domain changes in CNN networks and the optimization method for selecting thresholds in classification tasks

A visualization method for data domain changes in CNN networ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Minzhe Huang Changwei Nie Weihong Zhong Akuvox

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, Face Anti-Spoofing (FAS) has played a crucial role in preserving the security of face recognition technology. With the rise of counterfeit face generation techniques, the challenge posed by digitally edited faces to face anti-spoofing is escalating. Existing FAS technologies primarily focus on intercepting physically forged faces and lack a robust solution for cross-domain FAS challenges. Moreover, determining an appropriate threshold to achieve optimal deployment results remains an issue for intra-domain FAS. To address these issues, we propose a visualization method that intuitively reflects the training outcomes of models by visualizing the prediction results on datasets. Additionally, we demonstrate that employing data augmentation techniques, such as downsampling and Gaussian blur, can effectively enhance performance on cross-domain tasks. Building upon our data visualization approach, we also introduce a methodology for setting threshold values based on the distribution of the training dataset. Ultimately, our methods secured us second place in both the Unified Physical-Digital Face Attack Detection competition and the Snapshot Spectral Imaging Face Anti-spoofing contest. The training code is available at https://***/SeaRecluse/CVPRW2024.

关键词： Training computer vision Face recognition conferences Data visualization Imaging Optimization methods

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A Comprehensive Analysis of Factors Impacting Membership Inference

A Comprehensive Analysis of Factors Impacting Membership Inf...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Daniel DeAlcala Gonzalo Mancera Aythami Morales Julian Fierrez Ruben Tolosana Javier Ortega-Garcia Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We analyze various factors affecting the proper functioning of MIA and MINT, two research lines aimed at detecting data used for training. The difference between these lines lies in the environmental conditions, while the fundamental bases are similar for both. As evident in the literature, this detection task is far from straightforward and poses an ongoing challenge for the scientific community. Specifically, in this work, we conclude that factors such as the number of times data passes through the original network, the loss function, or dropout significantly impact detection outcomes. Therefore, it is crucial to consider them when developing these methods and during the training of any neural network, both to avoid (MIA) and to enhance (MINT) this detection. We evaluate the AdaFace facial recognition model using five databases with over 22 million images, modifying the different factors under analysis and defining a suitable protocol for their examination. State-of-the-art accuracy reaching up to 87% is achieved, surpassing existing methods.

关键词： Training Analytical models Accuracy Protocols Databases Face recognition Biological system modeling

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vision Transformers with Mixed-Resolution Tokenization

Vision Transformers with Mixed-Resolution Tokenization

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tomer Ronen Omer Levy Avram Golbert Tel Aviv University Google Research

vision Transformer models process input images by dividing them into a spatially regular grid of equal-size patches. Conversely, Transformers were originally introduced over natural language sequences, where each token represents a subword – a chunk of raw data of arbitrary size. In this work, we apply this approach to vision Transformers by introducing a novel image tokenization scheme, replacing the standard uniform grid with a mixed-resolution sequence of tokens, where each token represents a patch of arbitrary size. Using the Quadtree algorithm and a novel saliency scorer, we construct a patch mosaic where low-saliency areas of the image are processed in low resolution, routing more of the model’s capacity to important image regions. Using the same architecture as vanilla ViTs, our Quadformer models achieve substantial accuracy gains on image classification when controlling for the computational budget. Code and models are publicly available at https://***/TomerRonen34/mixed-resolution-vit.

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

引用

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

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 ieee.

关键词： Image enhancement

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uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories

uTRAND: Unsupervised Anomaly Detection in Traffic Trajectori...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Giacomo D’Amicantonio Egor Bondarau Peter H.N De With Eindhoven University of Technology Eindhoven Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper addresses two issues: the lack of labeled data and the difficulty of explaining the predictions of a neural network. To this end, we present a framework called uTRAND, that shifts the problem of anomalous trajectory prediction from the pixel space to a semantic-topological domain. The framework detects and tracks all types of traffic agents in bird’s-eye-view videos of traffic cameras mounted at an intersection. By conceptualizing the intersection as a patch-based graph, it is shown that the framework learns and models the normal behaviour of traffic agents without costly manual labeling. Furthermore, uTRAND allows to formulate simple rules to classify anomalous trajectories in a way suited for human interpretation. We show that uTRAND outperforms other state-of-the-art approaches on a dataset of anomalous trajectories collected in a real-world setting, while producing explainable detection results.

关键词： Accuracy Semantics Neural networks Layout Object detection Manuals Rendering (computer graphics)

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How SAM Perceives Different mp-MRI Brain Tumor Domains?

How SAM Perceives Different mp-MRI Brain Tumor Domains?

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cecilia Diana-Albelda Roberto Alcover-Couso Álvaro García-Martín Jesus Bescos Universidad Autónoma de Madrid

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Gliomas, among the deadliest forms of cancer, are brain tumors that present a significant challenge due to their rapid progression and resistance to treatment. Effective and early diagnosis is critical for improving patient prognosis. Deep learning, particularly through large-scale vision models like Segment Anything Model (SAM), offers a new pathway for tumor segmentation. This study seeks to address the primary challenge of adapting SAM for mp-MRI brain scans, which typically encompass multiple imaging modalities not fully utilized by standard three-channel vision models. We demonstrate that leveraging all available MRI modalities achieves superior performance compared to the standard mechanism of repeating a MRI scan to fit the input embedding. Our research also focuses on parameter-efficient tuning of SAM to effectively train the model while minimizing resource usage, showcasing significant improvements when evaluated across multiple datasets. Finally, we expose how SAM perceives differences across varied brain tumor domains by visually analyzing the features extracted on each of them. Our code and models are available at ***/vpulab/med-sam-brain.

关键词： Adaptation models Image segmentation Magnetic resonance imaging Feature extraction Brain modeling pattern recognition Prognostics and health management

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Drone-HAT: Hybrid Attention Transformer for Complex Action recognition in Drone Surveillance Videos

Drone-HAT: Hybrid Attention Transformer for Complex Action R...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mustaqeem Khan Jamil Ahmad Abdulmotaleb El Saddik Wail Gueaieb Giulia De Masi Fakhri Karray MBZUAI Abu Dhabi UAE University of Ottawa Canada Technology Innovation Institute UAE University of Waterloo Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Ultra-high-resolution aerial videos are becoming increasingly popular for enhancing surveillance capabilities in sparsely populated areas. However, analyzing human activities automatically, such as "who is doing what?" in these videos, is desirable to realize their surveillance potential. In contrast, atomic visual action detection has successfully recognized such activities in movie data. However, adapting it to ultra-high resolution aerial videos is challenging because the target persons appear relatively tiny from overhead views and are sparsely located. Additionally, existing atomic visual action detection methods are based on single-label actions. However, people can perform multiple actions simultaneously, so a multi-label approach would be more appropriate. To address these problems, we propose a multi-label action detection/recognition framework using a hybrid attention vision transformer (HAT) to recognize recurrent actions more efficiently. Additionally, a multi-scale, multi-granularity module inside the action recognition transformer block extracts relevant features without redundancy. Using the Okutama Dataset, we demonstrated that our method performs better than existing state-of-the-art methodologies for interpreting aerial videos for human activity.

关键词： Training Visualization computer vision Surveillance Termination of employment Transformers Feature extraction

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Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Surv...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Marcos V. Conde Florin-Alexandru Vasluianu Radu Timofte Jianxing Zhang Jia Li Fan Wang Xiaopeng Li Zikun Liu Hyunhee Park Sejun Song Changho Kim Zhijuan Huang Hongyuan Yu Cheng Wan Wending Xiang Jiamin Lin Hang Zhong Qiaosong Zhang Yue Sun Xuanwu Yin Kunlong Zuo Senyan Xu Siyuan Jiang Zhijing Sun Jiaying Zhu Liangyan Li Ke Chen Yunzhe Li Yimo Ning Guanhua Zhao Jun Chen Jinyang Yu Kele Xu Qisheng Xu Yong Dou CAIDAS & IFI Computer Vision Lab. University of Würzburg Samsung Research China - Beijing (SRC-B) Samsung MX(Mobile eXperience) Business Xiaomi Inc. Georgia Institute of Technology University of Science and Technology of China McMaster University Computer Dept. National University of Defense Technology Changsha China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 sub-missions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.

关键词： Surveys Degradation computer vision Reviews conferences Superresolution Pipelines Noise pattern recognition

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