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NTIRE 2022 Challenge on Perceptual Image Quality Assessment

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

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

作者： Gu, Jinjin Cai, Haoming Dong, Chao Ren, Jimmy S. Timofte, Radu Gong, Yuan Lao, Shanshan Shi, Shuwei Wang, Jiahao Yang, Sidi Wu, Tianhe Xia, Weihao Yang, Yujiu Cao, Mingdeng Heng, Cong Fu, Lingzhi Zhang, Rongyu Zhang, Yusheng Wang, Hao Song, Hongjian Wang, Jing Fan, Haotian Hou, Xiaoxia Sun, Ming Li, Mading Zhao, Kai Yuan, Kun Kong, Zishang Wu, Mingda Zheng, Chuanchuan Conde, Marcos, V Burchi, Maxime Feng, Longtao Zhang, Tao Li, Yang Xu, Jingwen Wang, Haiqiang Liao, Yiting Li, Junlin Xu, Kele Sun, Tao Xiong, Yunsheng Keshari, Abhisek Komal, Komal Thakur, Sadbhawana Jakhetiya, Vinit Subudhi, Badri N. Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Tiwari, Anil Kumar Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China SenseTime Res Beijing Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China UCL London England Netease Hangzhou Peoples R China Kuaishou Beijing Peoples R China Univ Wurzburg Comp Vis Lab Wurzburg Germany ByteDance Beijing Peoples R China Key Lab Parallel & Distributed Proc Beijing Peoples R China Indian Inst Technol Jammu Jagti Jammu & Kashmir India Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata W Bengal India Indian Inst Technol Jodhpur Jodhpur Rajasthan India Natl Taiwan Univ New Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate state-of-the-art performance.

关键词： Image quality computer vision conferences Computational modeling Market research Distortion pattern recognition

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Leveraging Future Trajectory Prediction for Multi-Camera People Tracking

Leveraging Future Trajectory Prediction for Multi-Camera Peo...

引用

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

作者： Jeon, Yuntae Tran, Dai Quoc Park, Minsoo Park, Seunghee Sungkyunkwan University Department of Global Smart City Korea Republic of Sungkyunkwan University Global Engineering Insitute for Ultimate Society Korea Republic of Sungkyunkwan University Sungkyun AI Research Institute Korea Republic of Architectural Engineering and Landscape Architecture Sungkyunkwan University School of Civil Korea Republic of

ISBN: (纸本)9798350302493

Artificial intelligence-based surveillance system, one of the essential systems for smart cities, plays a critical role in ensuring the safety and well-being of individuals. In this paper, we propose a real-time, low-computation cost Multi-Camera Multi-Target (MCMT) tracking system for people, leveraging deep-learning-based trajectory prediction with spatial-temporal information and social information. By predicting people's future trajectories, our algorithm effectively handles object occlusion problems and maintains accurate tracking while keeping computational costs low. Our approach addresses object occlusion without relying on computationally expensive re-identification, and improves MCMT tracking performance using graph-based tracklet representation, and spectral clustering. As a result, our proposed approach is tested on the 2023 AI City Challenge Track 1 test dataset, automatically generated on the NVIDIA Omiverse Platform, our method achieves an IDF1 score of 0.6171 and real-time performance at 27.6 FPS. Code and pre-trained models are publicly available at https://***/yuntaeJ/SCIT-MCMT-Tracking. © 2023 ieee.

关键词： Trajectories

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PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet Laws

PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet La...

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

作者： Wang, Bor-Shiun Chen, Ping-Yang Hsieh, Yi-Kuan Hsieh, Jun-Wei Chang, Ming-Ching He, JiaXin Teng, Shin-You Yue, HaoYuan Tseng, Yu-Chee National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Yang Ming Chiao Tung University College of Ai and Green Energy Tainan71150 Taiwan University at Albany State University of New York Department of Computer Science NY12222 United States Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798350302493

We present a video analytic system for enforcing motorcycle helmet regulation as a participation to the AI City Challenge 2023 [18] Track 5 contest. The advert of powerful object detectors enables real-time localization of the road users and even the ability to determine if a motorcyclist or a rider is wearing a helmet. Ensuring road safety is important, as the helmets can effectively provide protection against severe injuries and fatalities. However, monitoring and enforcing helmet compliance is challenging, given the large number of motorcyclists and limited visual input such as occlusions. To address these challenges, we propose a novel two-step approach. First, we introduce the PRB-FPN+, a state-of-the-art detector that excels in object localization. We also explore the benefits of deep supervision by incorporating auxiliary heads within the network, leading to enhanced performance of our deep learning architectures. Second, we utilize an advanced tracker named SMILEtrack to associate and refine the target track-lets. Comprehensive experimental results demonstrate that the PRB-FPN+ outperforms the state-of-the-art detectors on MS-COCO. Our system achieved a remarkable rank of 8 on the AI City Challenge 2023 [18] Track 5 Public Leader-board. Code implementation is available at: https://***/NYCU-AICVLab/AICITY-2023-Track5. © 2023 ieee.

关键词： Object detection

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3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes

3D-IntPhys: Towards More Generalized 3D-grounded Visual Intu...

引用

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

作者： Xue, Haotian Torralba, Antonio Tenenbaum, Joshua Yamins, Daniel Li, Yunzhu Tung, Hsiao-Yu Georgia Tech United States Mit United States Stanford Univeristy United States

ISBN: (纸本)9798350302493

Given a visual scene, humans have strong intuitions about how a scene can evolve over time under given actions. The intuition, often termed visual intuitive physics, is a critical ability that allows us to make effective plans to manipulate the scene to achieve desired outcomes without relying on extensive trial and error. In this paper, we present a framework capable of learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids. Our method is composed of a conditional Neural Radiance Field (NeRF)-style visual frontend and a 3D point-based dynamics prediction backend, using which we can impose strong relational and structural inductive bias to capture the structure of the underlying environment. Unlike existing intuitive point-based dynamics works that rely on the supervision of dense point trajectory from simulators, we relax the requirements and only assume access to multi-view RGB images and (imperfect) instance masks acquired using color prior. This enables the proposed model to handle scenarios where accurate point estimation and tracking are hard or impossible. We generate datasets including three challenging scenarios involving fluid, granular materials, and rigid objects in the simulation. The datasets do not include any dense particle information so most previous 3D-based intuitive physics pipelines can barely deal with that. We show our model can make long-horizon future predictions by learning from raw images and significantly outperforms models that do not employ an explicit 3D representation space. We also show that once trained, our model can achieve strong generalization in complex scenarios under extrapolate settings. © 2023 ieee.

关键词： computer vision

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NTIRE 2023 Video Colorization Challenge

NTIRE 2023 Video Colorization Challenge

引用

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

作者： Kang, Xiaoyang Lin, Xianhui Zhang, Kai Hui, Zheng Xiang, Wangmeng He, Jun-Yan Li, Xiaoming Ren, Peiran Xie, Xuansong Timofte, Radu Pan, Jinshan Peng, Zhongzheng Zhang, Qiyan Dong, Jiangxin Tang, Jinhui Li, Jinjing Lin, Chichen Li, Qipei Liang, Qirong Gang, Ruipeng Liu, Xiaofeng Feng, Shuang Liu, Shuai Wang, Hao Feng, Chaoyu Bai, Furui Zhang, Yuqian Shao, Guangqi Wang, Xiaotao Lei, Lei Chen, Siqi Zhang, Yu Xu, Hanning Liu, Zheyuan Zhang, Zhao Luo, Yan Zuo, Zhichao Damo Academy Alibaba Group China Computer Vision Lab Eth Zürich Switzerland Nanyang Technological University Singapore University of Würzburg Germany Nanjing University of Science and Technology China Communication University of China Beijing100024 China Academy of Broadcasting Science Nrta Beijing100866 China Uhdtv Research and Application Laboratory Beijing100176 China Xiaomi Inc. China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Zjut China China

ISBN: (纸本)9798350302493

This paper reviews the video colorization challenge on the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2023. The target of this challenge is converting grayscale videos into color videos with better colorization performance and temporal consistency. The challenge consists of two tracks. For Track 1, the goal is achieving the best FID (Fréchet Inception Distance) while being constrained to maintain or improve over the baseline method in terms of the temporal-consistency metric. The Color Distribution Consistency (CDC) index is used as the temporal consistency evaluation metric in this challenge. For Track 2, the target is to obtain a solution with the best CDC result while being constrained to maintain or improve over the baseline method in terms of FID. We use DeOldify-video as the baseline method for two tracks. For the final testing phase of both tracks, six teams submitted fact sheets and executable code of their solutions. This report brings together descriptions and discussions of all these solutions. Both tracks use the same data and the datasets are available at this url. © 2023 ieee.

关键词： Image enhancement

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Density Map Distillation for Incremental Object Counting

Density Map Distillation for Incremental Object Counting

引用

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

作者： Wu, Chenshen Van De Weijer, Joost Computer Vision Center Barcelona Spain

ISBN: (纸本)9798350302493

We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. © 2023 ieee.

关键词： Distillation

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BeautyREC: Robust, Efficient, and Component-Specific Makeup Transfer

BeautyREC: Robust, Efficient, and Component-Specific Makeup ...

引用

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

作者： Yan, Qixin Guo, Chunle Zhao, Jixin Dai, Yuekun Loy, Chen Change Li, Chongyi Tencent WeChat China Nankai University TMCC CS China Nanyang Technological University S-Lab Singapore

ISBN: (纸本)9798350302493

In this work, we propose a Robust, Efficient, and Component-specific makeup transfer method (abbreviated as BeautyREC). A unique departure from prior methods that leverage global attention, simply concatenate features, or implicitly manipulate features in latent space, we propose a component-specific correspondence to directly transfer the makeup style of a reference image to the corresponding components (e.g., skin, lips, eyes) of a source image, making elaborate and accurate local makeup transfer. As an auxiliary, the long-range visual dependencies of Transformer are introduced for effective global makeup transfer. Instead of the commonly used cycle structure that is complex and unstable, we employ a content consistency loss coupled with a content encoder to implement efficient single-path makeup transfer. The key insights of this study are modeling component-specific correspondence for local makeup transfer, capturing long-range dependencies for global makeup transfer, and enabling efficient makeup transfer via a single-path *** also contribute BeautyFace, a makeup transfer dataset to supplement existing datasets. This dataset contains 3,000 faces, covering more diverse makeup styles, face poses, and races. Each face has annotated parsing map. Extensive experiments demonstrate the effectiveness of our method against state-of-the-art methods. Besides, our method is appealing as it is with only 1M parameters, outperforming the state-of-the-art methods (BeautyGAN: 8.43M, PSGAN: 12.62M, SCGAN: 15.30M, CPM: 9.24M, SSAT: 10.48M). © 2023 ieee.

关键词： computer vision

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Surveillance Face Presentation Attack Detection Challenge

Surveillance Face Presentation Attack Detection Challenge

引用

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

作者： Fang, Hao Liu, Ajian Wan, Jun Escalera, Sergio Escalante, Hugo Jair Lei, Zhen University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Chinese Academy of Sciences Mais Institute of Automation Beijing China Universitat de Barcelona Computer Vision Center Catalonia Barcelona Spain Óptica y Electrónica JInstituto Nacional de Astrofísica Puebla Mexico Cair Hkisi Cas

ISBN: (纸本)9798350302493

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios. © 2023 ieee.

关键词： Face recognition

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Disentangling Local and Global Information for Light Field Depth Estimation

Disentangling Local and Global Information for Light Field D...

引用

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

作者： Yang, Xueting Deng, Junli Chen, Rongshan Cong, Ruixuan Ke, Wei Sheng, Hao Communication University of China School of Information and Communication Engineering Beijing100024 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Accurate depth estimation from light field images is essential for various applications. Deep learning-based techniques have shown great potential in addressing this problem while still face challenges such as sensitivity to occlusions and difficulties in handling untextured areas. To overcome these limitations, we propose a novel approach that utilizes both local and global features in the cost volume for depth estimation. Specifically, our hybrid cost volume network consists of two complementary sub-modules: a 2D ContextNet for global context information and a matching cost volume for local feature information. We also introduce an occlusion-aware loss that accounts for occlusion areas to improve depth estimation quality. We demonstrate the effectiveness of our approach on the UrbanLF and HCInew datasets, showing significant improvements over existing methods, especially in occluded and untextured regions. Our method disentangles local feature and global semantic information explicitly, reducing the occlusion and untextured area reconstruction error and improving the accuracy of depth estimation. © 2023 ieee.

关键词： Semantics

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Certified Adversarial Robustness Within Multiple Perturbation Bounds

Certified Adversarial Robustness Within Multiple Perturbatio...

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

作者： Nandi, Soumalya Addepalli, Sravanti Rangwani, Harsh Babu, R. Venkatesh Indian Institute of Science Vision and AI Lab Bengaluru India

ISBN: (纸本)9798350302493

Randomized smoothing (RS) is a well known certified defense against adversarial attacks, which creates a smoothed classifier by predicting the most likely class under random noise perturbations of inputs during inference. While initial work focused on robustness to 2 norm perturbations using noise sampled from a Gaussian distribution, subsequent works have shown that different noise distributions can result in robustness to other p norm bounds as well. In general, a specific noise distribution is optimal for defending against a given p norm based attack. In this work, we aim to improve the certified adversarial robustness against multiple perturbation bounds simultaneously. Towards this, we firstly present a novel certification scheme, that effectively combines the certificates obtained using different noise distributions to obtain optimal results against multiple perturbation bounds. We further propose a novel training noise distribution along with a regularized training scheme to improve the certification within both 1 and 2 perturbation norms simultaneously. Contrary to prior works, we compare the certified robustness of different training algorithms across the same natural (clean) accuracy, rather than across fixed noise levels used for training and certification. We also empirically invalidate the argument that training and certifying the classifier with the same amount of noise gives the best results. The proposed approach achieves improvements on the ACR (Average Certified Radius) metric across both 1 and 2 perturbation bounds. Code available at https://***/valiisc/NU-Certified-Robustness © 2023 ieee.

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