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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

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Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery

Performance Evaluation of Segment Anything Model with Variat...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yona Falinie A. Gaus Neelanjan Bhowmik Brian K. S. Isaac-Medina Toby P. Breckon Department of Computer Science Durham University UK Department of Engineering Durham University UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM) is a deep neural network foundational model designed to perform instance segmentation which has gained significant popularity given its zero-shot segmentation ability. SAM operates by generating masks based on various input prompts such as text, bounding boxes, points, or masks, introducing a novel methodology to overcome the constraints posed by dataset-specific scarcity. While SAM is trained on an extensive dataset, comprising more than 11M images, it mostly consists of natural photographic (visible band) images with only very limited images from other modalities. Whilst the rapid progress in visual infrared surveillance and X-ray security screening imaging technologies, driven forward by advances in deep learning, has significantly enhanced the ability to detect, classify and segment objects with high accuracy, it is not evident if the SAM zero-shot capabilities can be transferred to such modalities beyond the visible spectrum. For this reason, this work comprehensively assesses SAM capabilities in segmenting objects of interest in the X-ray and infrared imaging modalities. Our approach reuses and preserves the pre-trained SAM with three different prompts, namely bounding box, centroid and random points. We present several quantitative and qualitative results to showcase the performance of SAM on selected datasets. Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts. Specifically, SAM performs poorly in segmenting slender objects and organic materials, such as plastic bottles. Additionally, we find that infrared objects are also challenging to segment with point prompts given the low-contrast nature of this modality. Overall, this study shows that while SAM demonstrates outstanding zero-shot capabilities with box prompts, its performance ranges from moderate to poor for point prompts, indicating that special consideration on the cross-modal gener

关键词： Image segmentation Visualization Infrared surveillance Accuracy Annotations Organic materials Streaming media

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Online Unsupervised Domain Adaptation for Person Re-identification

Online Unsupervised Domain Adaptation for Person Re-identifi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hamza Rami Matthieu Ospici Sté phane Lathuiliè re LTCI T&#x00E9 l&#x00E9 com Paris Institut Polytechnique de Paris Atos

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical real-world application, where data is continuously increasing from the different network’s cameras. The Re-ID solutions are also constrained by confidentiality regulations stating that the collected data can be stored for only a limited period, hence the model can no longer get access to previously seen target images. Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on this new online setting using the well-known Market-1501, Duke, and MSMT17 benchmarks.

关键词： Training Adaptation models Privacy computer vision conferences Benchmark testing Data models

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NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Stereo Image Super-Resolution: Metho...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Cheng, Ming Ma, Haoyu Ma, Qiufang Sun, Xiaopeng Zhao, Shijie Sheng, Xuhan Ding, Yukan Sun, Ming Wen, Xing Zhang, Dafeng Li, Jia Wang, Fan Xie, Zheng He, Zongyao Qiu, Zidian Pan, Zilin Zhan, Zhihao Xian, Xingyuan Jin, Zhi Zhou, Yuanbo Deng, Wei Nie, Ruofeng Zhang, Jiajun Gao, Qinquan Tong, Tong Zhang, Kexin Zhang, Junpei Peng, Rui Ma, Yanbiao Jiao, Licheng Bai, Haoran Kong, Lingshun Pan, Jinshan Dong, Jiangxin Tang, Jinhui Cao, Pu Huang, Tianrui Yang, Lu Song, Qing Chen, Bingxin He, Chunhua Chen, Meiyun Guo, Zijie Luo, Shaojuan Cao, Chengzhi Wang, Kunyu Zhang, Fanrui Zhang, Qiang Mehta, Nancy Murala, Subrahmanyam Dudhane, Akshay Wang, Yujin Li, Lingen Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Chen, Junyang Li, Hao Shi, Yukai Yang, Zhijing Zou, Wenbin Zhang, Yunchen Jiang, Mingchao Yu, Zhongxin Tan, Ming Gao, Hongxia Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Chen, Jingxiang Yang, Bo Zhang, Xisheryl Li, Chenghua Yuan, Weijun Li, Zhan Deng, Ruting Zeng, Jintao Mahajan, Pulkit Mistry, Sahaj Chatterjee, Shreyas Jakhetiya, Vinit Subudhi, Badri Jaiswal, Sunil Zhang, Zhao Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Wang, Bo Li, Gen Li, Aijin Sun, Lei Chen, Ke Tang, Congling Li, Yunzhe Chiang, Yuan-Chun Chen, Yi-Chung Huang, Zhi-Kai Yang, Hao-Hsiang Chen, I-Hsiang Kuo, Sy-Yen Wang, Yiheng Zhu, Gang Yang, Xingyi Liu, Songhua Jing, Yongcheng Hu, Xingyu Song, Jianwen Sun, Changming Sowmya, Arcot Park, Seung Ho Lei, Xiaoyan Wang, Jingchao Zhai, Chenbo Zhang, Yufei Cao, Weifeng Zhang, Wenlong Aviation University of Air Force China The Shenzhen Campus of Sun Yat-sen University Sun Yatsen University China National University of Defense Technology China Shanghai University China University of Electronic Science and Technology of China China University of Würzburg Germany ETH Zürich Switzerland ByteDance China Kuaishou Technology China Samsung Research China - Beijing China Sun Yat-sen University China Fuzhou University China Imperial Vision Technology Xidian University China Nanjing University of Science and Technology China Beijing University of Posts and Telecommunications China Guangdong University of Technology China The Hong Kong Polytechnic University Hong Kong University of Science and Technology of China China Indian Institute of Technology Ropar India MBZUAI Dubai United Arab Emirates Shanghai Artificial Intelligence Laboratory China Shanghai Jiao Tong University China Assiut University Egypt South China University of Technology China Fujian Normal University China GAC R&D Center Uppsala University Sweden Nanjing University of Information Science and Technology China Chinese Academy of Sciences Institute of Automation China Jinan University China Jiangxi Normal University China Indian Institute of Technology India K-Lens GmbH Hefei University of Technology China McMaster University Canada National Taiwan University Taiwan Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Harbin Institute of Technology China University of New South Wales Australia CSIRO Data61 Australia Seoul National University Korea Republic of Zhengzhou University of Light Industry China

ISBN: (纸本)9798350302493

This paper summarizes the 2nd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of the challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of ×4. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 3 tracks, including one track on distortion (e.g., PSNR) and bicubic degradation, one track on perceptual quality (e.g., LPIPS) and bicubic degradation, as well as another track on real degradations. In total, 175, 93, and 103 participants were successfully registered for each track, respectively. In the test phase, 21, 17, and 12 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR. © 2023 ieee.

关键词： Optical resolving power

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Multi-Camera Multi-Vehicle Tracking with Domain Generalization and Contextual Constraints

Multi-Camera Multi-Vehicle Tracking with Domain Generalizati...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nhat Minh Chung Huy Dinh-Anh Le Vuong Ai Nguyen Quang Qui-Vinh Nguyen Thong Duy-Minh Nguyen Tin-Trung Thá i Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In this paper, we propose a system for Multi-Camera Multi-Target (MCMT) Vehicle Tracking in Track 1 of AI City Challenge 2022. There are many technical difficulties to the MCMT problem such as a common lack of labeled data in real scenarios, a distortion of vehicle detailed appearances in recording, and ambiguity between highly similar vehicles. Taking those into account, we develop a 3-component MCMT system that exploits vehicle behavior, leverages synthetic data and multiple augmentation techniques, and enforces contextual constraints. Specifically, our system involves a motion-driven vehicle tracker for obtaining robust trajectories, applying MixStyle domain generalization on the TransReID model to exploit as much labeled data as possible, and experimenting with contextual constraints such as our proposed neighbor matching to address ambiguity in terms of vehicle appearances. Overall, our system achieved an IDF1 score of 0.7255.

关键词： Training Tracking conferences Urban areas Distortion Data models Trajectory

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SomethingFinder: Localizing undefined regions using referring expressions

SomethingFinder: Localizing undefined regions using referrin...

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

作者： Eum, Sungmin Han, David Briggs, Gordon US Army Res Lab Adelphi MD 20783 USA Booz Allen Hamilton Mclean VA 22102 USA US Naval Res Lab Washington DC USA

ISBN: (纸本)9781728193601

Previous research on localizing a target region in an image referred to by a natural language expression has occurred within an object-centric paradigm. However, in practice, there may not be any easily named or identifiable objects near a target location. Instead, references may need to rely on basic visual attributes, such as color or geometric clues. An expression like "a red something beside a blue vertical line" could still pinpoint a target location. As such, we begin to explore the open challenge of computational object-agnostic reference by constructing a novel dataset and by devising a new set of algorithms that can identify a target region in an image when given a referring expression containing only basic conceptual features.

关键词： computer vision

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Simplifying Transformations for a Family of Elastic Metrics on the Space of Surfaces

Simplifying Transformations for a Family of Elastic Metrics ...

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

作者： Su, Zhe Bauer, Martin Klassen, Eric Gallivan, Kyle Florida State Univ Dept Math Tallahassee FL 32306 USA

ISBN: (纸本)9781728193601

We define a new representation for immersed surfaces in R-3 by combining the SRNF and the induced surface metric. Using the L-2 metric on the space of SRNFs and the DeWitt metric on the space of surface metrics, we obtain a 3-parameter family of metrics that corresponds to the family of "elastic metrics" proposed by Jermyn et al. in [19] on the space of immersed surfaces. Similar to the original SRNF representation this new representation results in an extrinsic distance function on the space of immersed surfaces that is easy to compute as it is given by an explicit formula. In addition to avoiding the degeneracy of the SRNF it allows for a data-driven choice of the parameters of the metric, while still providing for fast and accurate registration of surfaces.

关键词： computer vision

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CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Himalaya Jain Tuan-Hung Vu Patrick Pé rez Matthieu Cord Valeo.ai Paris France Sorbonne University Paris France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training Visualization Costs Semantics Memory management Generative adversarial networks pattern recognition

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Complete and temporally consistent video outpainting

Complete and temporally consistent video outpainting

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Loï c Dehan Wiebe Van Ranst Patrick Vandewalle Toon Goedemé EAVISE-PSI-ESAT KU Leuven Sint-Katelijne-Waver Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

We describe a novel method for video outpainting. The goal of outpainting is to fill in missing regions at the edges of video frames. Our focus lies on converting portrait (9:16) to landscape (16:9) video. In contrast, most video completion research is focused on inpainting: filling a masked section within the frame based on the remaining, known *** proposed method consists of three main aspects: (1) We form a background estimation using video object segmentation and video inpainting methods, (2) we use optical flow to form temporal consistency, and (3) we propose image shifting to improve individual frame completions. Our method is able to successfully broaden the aspect ratio of a video. On most videos, we achieve realistic results. Only on videos with complex camera motion and foreground objects leaving the frame, the quality is *** contrast to other state-of-the-art methods, our method is able to reconstruct the full frame, including unseen image parts. Moreover, it is temporally consistent. We evaluate our method on the DAVIS and YouTube-VOS datasets. The code is publicly available 1 .

关键词： Measurement computer vision Image edge detection conferences Estimation Object segmentation Filling

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NTIRE 2021 NonHomogeneous Dehazing Challenge Report

NTIRE 2021 NonHomogeneous Dehazing Challenge Report

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

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Fu, Minghan Liu, Huan Yu, Yankun Chen, Jun Wang, Keyan Chang, Jerome Wang, Xiyao Liu, Jing Xu, Yi Zhang, Xinjian Zhao, Minyi Zhou, Shuigeng Chen, Tianyi Fu, Jiahui Jiang, Wentao Gao, Chen Liu, Si Wang, Yudong Guo, Jichang Li, Chongyi Yan, Qixin Zheng, Sida Zamir, Syed Waqas Arora, Aditya Dudhane, Akshay Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zhang, Haichuan Guo, Tiantong Monga, Vishal Yang, Wenjin Lin, Jin Luo, Xiaotong Huang, Guowen Chen, Shuxin Qu, Yanyun Xu, Kele Yang, Lehan Sun, Pengliang Niu, Xuetong Zheng, Junjun Ruan, Xiaotong Wang, Yunfeng Yang, Jiang Luo, Zhipeng Wang, Sai Xu, Zhenyu Cao, Xiaochun Luo, Jun Zheng, Zhuoran Ren, Wenqi Wang, Tao Chen, Yiqun Leng, Cong Li, Chenghua Cheng, Jian Sung, Chang-Sung Chen, Jun-Cheng Jo, Eunsung Sim, Jae-Young Geethu, M. M. Akhil, K. A. Sreeni, K. G. Jeena, R. S. Zacharias, Joseph Manu, Chippy M. Huang, Zexi Zhang, Baofeng Zhang, Yiwen Li, Jindong Chen, Mianjie Xiao, Quan Su, Qingchao Han, Lihua Huang, Yanting Prajapati, Kalpesh Chudasama, Vishal Patel, Heena Sarvaiya, Anjali Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Jing, Hongyuan Huang, Zilong Fu, Yiran Wu, Haoqiang Zha, Quanxing Zhu, Zhiwei Lv, Hejun Univ Politehn Timisoara ETcTI Timisoara Romania UCL ICTEAM Louvain Belgium Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland McMaster Univ Hamilton ON Canada Xidian Univ Xian Peoples R China Natl Taiwan Univ Taipei Taiwan Univ Chinese Acad Sci Inst Automat Ctr Res Intelligent Syst & Engn Beijing Peoples R China Bilibili Inc Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Beihang Univ Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn Singapore Singapore Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates Penn State Univ Sch Elect Engn & Comp Sci Informat Proc & Algorithms Lab iPAL University Pk PA 16802 USA Xiamen Univ Dept Comp Sci Xiamen Peoples R China Key Lab Parallel & Distributed Proc Changsha Peoples R China Alibaba Inc Shanghai Peoples R China DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China CASIA Beijing Peoples R China CASIA AiRiA Beijing Peoples R China Natl Taiwan Univ Data Sci Degree Program Taipei Taiwan Ulsan Natl Inst Sci & Technol Ulsan South Korea Coll Engn Dept ECE CV Lab Trivandrum Kerala India AICTE Univ Kollam Kerala India South China Univ Techonol Sch Elect & Informat Engn Guangzhou Peoples R China Shenzhen Wave Kingdom Co Ltd Shenzhen Peoples R China Sardar Vallabhbhai Natl Inst Technol Surat India Beijing Union Univ Coll Robot Beijing Peoples R China

ISBN: (纸本)9781665448994

This work reviews the results of the NTIRE 2021 Challenge on Non-Homogeneous Dehazing. The proposed techniques and their results have been evaluated on a novel dataset that extends the NH-Haze datset. It consists of additional 35 pairs of real haze free and nonhomogeneous hazy images recorded outdoor. The nonhomogeneous haze has been introduced in the outdoor scenes by using a a professional setup that imitates the real conditions of haze scenes. 327 participants registered in the challenge and 23 teams competed in the final testing phase. The proposed solutions gauge the state-of-the-art in image dehazing.

关键词： computer vision Correlation conferences Architecture computer architecture pattern recognition Testing

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Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marcus Valtonen Ö rnhag Patrik Persson Må rten Wadenbä ck Kalle Å strö m Anders Heyden Centre for Mathematical Sciences Lund University Department of Electrical Engineering Link&#x00F6 ping University

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated *** proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://***/marcusvaltonen/DronePoseLib. 1

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

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