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NTIRE 2021 Depth Guided Image Relighting Challenge

NTIRE 2021 Depth Guided Image Relighting Challenge

引用

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

作者： El Helou, Majed Zhou, Ruofan Susstrunk, Sabine Timofte, Radu Suin, Maitreya Rajagopalan, A. N. Wang, Yuanzhi Lu, Tao Zhang, Yanduo Wu, Yuntao Yang, Hao-Hsiang Chen, Wei-Ting Kuo, Sy-Yen Luo, Hao-Lun Zhang, Zhiguang Luo, Zhipeng He, Jianye Zhu, Zuo-Liang Li, Zhen Qiu, Jia-Xiong Kuang, Zeng-Sheng Lu, Cheng-Ze Cheng, Ming-Ming Shao, Xiu-Li Li, Chenghua Ding, Bosong Qian, Wanli Li, Fangya Li, Fu Deng, Ruifeng Lin, Tianwei Liu, Songhua Li, Xin He, Dongliang Yazdani, Amirsaeed Guo, Tiantong Monga, Vishal Nsampi, Ntumba Elie Hu, Zhongyun Wang, Qing Nathan, Sabari Kansal, Priya Zhao, Tongtong Zhao, Shanshan Ecole Polytech Fed Lausanne Lausanne Switzerland Swiss Fed Inst Technol Zurich Switzerland ASUSTeK Comp Inc ASUS Intelligent Cloud Serv Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Chinese Acad Sci Inst Automat Beijing Peoples R China Couger Inc Tokyo Japan DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Penn State Univ Sch Elect Engn & Comp Sci Informat Proc & Algorithms Lab iPAL University Pk PA 16802 USA Indian Inst Technol Madras Chennai Tamil Nadu India Nankai Univ Tianjin Peoples R China Northwestern Polytech Univ Sch Comp Sci Comp Vis & Computat Photog Grp Xian Peoples R China Dalian Maritime Univ Dalian Peoples R China China Everbright Bank Shanghai Peoples R China Baidu Inc Dept Comp Vis Vis Beijing Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan Peoples R China

ISBN: (纸本)9781665448994

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper.

关键词： Geometry computer vision Target tracking Image color analysis conferences Lighting Transforms

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ART•V: Auto-Regressive Text-to-Video Generation with Diffusion Models

ART•V: Auto-Regressive Text-to-Video Generation with Diffus...

引用

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

作者： Wenming Weng Ruoyu Feng Yanhui Wang Qi Dai Chunyu Wang Dacheng Yin Zhiyuan Zhao Kai Qiu Jianmin Bao Yuhui Yuan Chong Luo Yueyi Zhang Zhiwei Xiong University of Science and Technology of China Microsoft Research Asia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present ART•V, an efficient framework for autoregressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-shot, ART•V generates a single frame at a time, conditioned on the previous ones. The framework offers three distinct advantages. First, it only learns simple continual motions between adjacent frames, therefore avoiding modeling complex long-range motions that require huge training data. Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications. Third, it can generate arbitrarily long videos conditioned on a variety of prompts such as text, image or their combinations, making it highly versatile and flexible. To combat the common drifting issue in AR models, we propose masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting. Moreover, we further enhance generation coherence by conditioning it on the initial frame, which typically contains minimal noise. This is particularly useful for long video generation. When trained for only two weeks on four GPUs, ART•V already can generate videos with natural motions, rich details and a high level of aesthetic quality. Besides, it enables various appealing applications, e.g. composing a long video from multiple text prompts.

关键词： computer vision conferences Noise Training data Coherence Predictive models Diffusion models

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NTIRE 2024 Challenge on Light Field Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Light Field Image Super-Resolution: ...

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

作者： Yingqian Wang Zhengyu Liang Qianyu Chen Longguang Wang Jungang Yang Radu Timofte Yulan Guo Wentao Chao Yiming Kan Xuechun Wang Fuqing Duan Guanghui Wang Wang Xia Ziqi Wang Yue Yan Peiqi Xia Shunzhou Wang Yao Lu Angulia Yang Kai Jin Zeqiang Wei Sha Guo Mingzhi Gao Xiuzhuang Zhou Zhongxin Yu Shaofei Luo Cheng Zhong Shaorui Chen Long Peng Yuhong He Gaosheng Liu Huanjing Yue Jingyu Yang Zhengjian Yao Jiakui Hu Lujia Jin Zhi-Song Liu Chenhang He Jun Xiao Xiuyuan Wang Zonglin Tian Yifan Mao Deyang Liu Shizheng Li Ping An

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this report, we summarize the 2nd NTIRE challenge on light field (LF) image super-resolution (SR) with a focus on new methods and results. This challenge aims at superresolving LF images under the standard bicubic downsampling degradation with a magnification factor of ×4. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. This year of challenge has two tracks, including one track on fidelity (i.e., restoration accuracy in terms of PSNR) only, and the other track on fidelity with an extra constraint on model size and computational cost. In total, 125 participants were successfully registered for this challenge, and 9 teams have successfully submitted results with PSNR scores higher than the baseline methods. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR.

关键词： Degradation computer vision conferences Computational modeling Superresolution Market research Light fields

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RLNet: Robust Linearized Networks for Efficient Private Inference

RLNet: Robust Linearized Networks for Efficient Private Infe...

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

作者： Sreetama Sarkar Souvik Kundu Peter A. Beereil Universiy of Southern California Los Angeles USA Intel Labs San Diego USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The growing concern about data privacy has led to the development of private inference (PI) frameworks in client-server applications which protects both data privacy and model IP. However, the cryptographic primitives required yield significant latency overheads which limits their wide-spread application. At the same time, changing environments demand PI services to be robust against various naturally occurring and gradient-based perturbations. Despite several works focused on the development of latency-efficient models suitable for PI, the impact of these models on robustness has remained unexplored. Towards this goal, this paper presents RLNet, a class of models that can yield latency improvement via the reduction of high-latency ReLU operations while improving the model performance on both clean and corrupted images. In particular, RLNet models provide a "triple win ticket" of improved classification accuracy on clean, naturally perturbed, and gradient- based perturbed images using a shared-mask shared-weight architecture with over an order of magnitude fewer ReLUs than baseline models. To demonstrate the efficacy of RLNet, we perform extensive experiments with ResNet and WRN model variants on CIFAR-10, CIFAR-100, and Tiny- ImageNet datasets. Our experimental evaluations show that RLNet can yield models with up to 11.14× fewer ReLUs, with accuracy close to the all-ReLU models, on clean, naturally perturbed, and gradient-based perturbed images. Compared with the SoTA non-robust linearized models at similar ReLU budgets, RLNet achieves an improvement in adversarial accuracy of up to ~47%, in naturally perturbed accuracy of up to ~16.4%, while improving clean image accuracy up to ~1.5%. Code is available at: https://***/sreetamasarkar/rlnet.

关键词： Data privacy computer vision Accuracy Computational modeling Perturbation methods Transformers Robustness

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T2LM: Long-Term 3D Human Motion Generation from Multiple Sentences

T2LM: Long-Term 3D Human Motion Generation from Multiple Sen...

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

作者： Taeryung Lee Fabien Baradel Thomas Lucas Kyoung Mu Lee Grégory Rogez IPAI & ASRI Naver LABS Europe Dept. of ECE Seoul National University Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a stream of multiple sentences (i.e., paragraph). Previous long-term motion generating approaches were mostly based on recurrent methods, using previously generated motion chunks as input for the next step. However, this approach has two drawbacks: 1) it relies on sequential datasets, which are expensive; 2) these methods yield unrealistic gaps between motions generated at each step. To address these issues, we introduce simple yet effective T2LM, a continuous long-term generation framework that can be trained without sequential data. T2LM comprises two components: a 1D-convolutional VQVAE, trained to compress motion to sequences of latent vectors, and a Transformer-based Text Encoder that predicts a latent sequence given an input text. At inference, a sequence of sentences is translated into a continuous stream of latent vectors. This is then decoded into a motion by the VQVAE decoder; the use of 1D convolutions with a local temporal receptive field avoids temporal inconsistencies between training and generated sequences. This simple constraint on the VQ-VAE allows it to be trained with short sequences only and produces smoother transitions. T2LM outperforms prior long-term generation models while overcoming the constraint of requiring sequential data; it is also competitive with SOTA single-action generation models.

关键词： Training computer vision Three-dimensional displays conferences Computational modeling Transformers Vectors

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NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge

NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challe...

引用

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

作者： Jie Liang Radu Timofte Qiaosi Yi Shuaizheng Liu Lingchen Sun Rongyuan Wu Xindong Zhang Hui Zeng Lei Zhang Yibin Huang Shuai Liu Yongqiang Li Chaoyu Feng Xiaotao Wang Lei Lei Yuxiang Chen Xiangyu Chen Qiubo Chen Fengyu Sun Mengying Cui Jiaxu Chen Zhenyu Hu Jingyun Liu Wenzhuo Ma Ce Wang Hanyou Zheng Wanjie Sun Zhenzhong Chen Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön Xiong Dun Pengzhou Ji Yujie Xing Xuquan Wang Zhanshan Wang Xinbin Cheng Jun Xiao Chenhang He Xiuyuan Wang Zhi-Song Liu Zimeng Miao Zhicun Yin Ming Liu Wangmeng Zuo Shuai Li

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications. The participants were required to restore the real-captured images from complex and unknown degradation, where generative perceptual quality and fidelity are desired in the restoration result. The challenge consisted of two tasks. Task one employed real referenced data pairs, where quantitative evaluation is available. Task two used unpaired images, and a comprehensive user study was conducted. The challenge attracted more than 200 registrations, where 39 of them submitted results with more than 400 submissions. Top-ranked methods improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges. The proposed datasets are available at https : //***/file/d/1DqbxUoiUqkAIkExu3jZAqoElr_nu1IXb/view?usp=sharing and the homepage of this challenge is at https : //***/competitions/17632.

关键词： Degradation computer vision Reviews conferences Computational modeling Benchmark testing Image restoration

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Area Under the ROC Curve Maximization for Metric Learning

Area Under the ROC Curve Maximization for Metric Learning

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

作者： Bojana Gajić Ariel Amato Ramon Baldrich Joost van de Weijer Carlo Gatta Vintra Inc. Barcelona Spain Computer Vision Center Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.

关键词： Training computer vision conferences Area measurement Benchmark testing pattern recognition

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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

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

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 ieee.

关键词： Demulsification

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Selective Multi-View Deep Model for 3D Object Classification

Selective Multi-View Deep Model for 3D Object Classification

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

作者： Mona Alzahrani Muhammad Usman Saeed Anwar Tarek Helmy Department of Information & Computer Science KFUPM Dhahran Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran Saudi Arabia Center for Intelligent Secure Systems KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

3D object classification has emerged as a practical technology with applications in various domains, such as medical image analysis, automated driving, intelligent robots, and crowd surveillance. Among the different approaches, multi-view representations for 3D object classification have shown the most promising results, achieving state-of-the-art performance. However, there are certain limitations in current view-based 3D object classification methods. One observation is that using all captured views for classification can confuse the classifier and lead to misleading results for certain classes. Additionally, some views may contain more discriminative information for object classification than others. These observations motivate the development of smarter and more efficient selective multi-view classification models. In this work, we propose a Selective Multi-View Deep Model that extracts multi-view images from 3D data representations and selects the most influential view by assigning importance scores using the cosine similarity method based on visual features detected by a pre-trained CNN. The proposed method is evaluated on the ModelNet40 dataset for the task of 3D classification. The results demonstrate that the proposed model achieves an overall accuracy of 88.13% using only a single view when employing a shading technique for rendering the views, pre-trained ResNet-152 as the backbone CNN for feature extraction, and a Fully Connected Network (FCN) as the classifier.

关键词： Training Solid modeling Visualization Three-dimensional displays Image analysis Surveillance Feature extraction

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RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth

RDONet: Rate-Distortion Optimized Learned Image Compression ...

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

作者： Fabian Brand Kristian Fischer Alexander Kopte Marc Windsheimer André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universit&#x00E4 t Erlangen-N&#x00FC rnberg Erlangen Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Rate-distortion optimization (RDO) is responsible for large gains in image and video compression. While RDO is a standard tool in traditional image and video coding, it is not yet widely used in novel end-to-end trained neural methods. The major reason is that the decoding function is trained once and does not have free parameters. In this paper, we present RDONet, a network containing state-of-the-art components, which is perceptually optimized and capable of rate-distortion optimization. With this network, we are able to outperform VVC Intra on MS-SSIM and two different perceptual LPIPS metrics. This paper is part of the CLIC challenge, where we participate under the team name RDONet FAU.

关键词： Measurement Video coding Training Image coding Rate-distortion Video compression pattern recognition

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