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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

引用

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

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 ieee.

关键词： Data acquisition

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Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness

Generating Adversarial Samples in Mini-Batches May Be Detrim...

引用

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

作者： Timothy Redgrave Colton Crum Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

Neural networks have been proven to be both highly effective within computer vision, and highly vulnerable to adversarial attacks. Consequently, as the use of neural networks increases due to their unrivaled performance, so too does the threat posed by adversarial attacks. In this work, we build towards addressing the challenge of adversarial robustness by exploring the relationship between the mini-batch size used during adversarial sample generation and the strength of the adversarial samples produced. We demonstrate that an increase in mini-batch size results in a decrease in the efficacy of the samples produced, and we draw connections between these observations and the phenomenon of vanishing gradients. Next, we formulate loss functions such that adversarial sample strength is not degraded by mini-batch size. Our findings highlight a potential risk for underestimating the true (practical) strength of adversarial attacks, and a risk of overestimating a model’s robustness. We share our codes to let others replicate our experiments and to facilitate further exploration of the connections between batch size and adversarial sample strength.

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Exploring the Benefits of vision Foundation Models for Unsupervised Domain Adaptation

Exploring the Benefits of Vision Foundation Models for Unsup...

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

作者： Brunó B. Englert Fabrizio J. Piva Tommie Kerssies Daan de Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safety-critical applications, where deep-neural-network-based systems must perform reliably under various environmental conditions not seen during training. Our study investigates whether the generalization capabilities of vision Foundation Models (VFMs) and Unsupervised Domain Adaptation (UDA) methods for the semantic segmentation task are complementary. Results show that combining VFMs with UDA has two main benefits: (a) it allows for better UDA performance while maintaining the out-of-distribution performance of VFMs, and (b) it makes certain time-consuming UDA components redundant, thus enabling significant inference speedups. Specifically, with equivalent model sizes, the resulting VFM-UDA method achieves an 8.4× speed increase over the prior non-VFM state of the art, while also improving performance by +1.2 mIoU in the UDA setting and by +6.1 mIoU in terms of out-of-distribution generalization. Moreover, when we use a VFM with 3.6× more parameters, the VFM-UDA approach maintains a 3.3× speed up, while improving the UDA performance by +3.1 mIoU and the out-of-distribution performance by +10.3 mIoU. These results underscore the significant benefits of combining VFMs with UDA, setting new standards and baselines for Unsupervised Domain Adaptation in semantic segmentation. The implementation is available at https://***/tue-mps/vfm-uda.

关键词： Training Adaptation models computer vision Semantic segmentation conferences Computational modeling pattern recognition

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Active Transferability Estimation

Active Transferability Estimation

引用

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

作者： Tarun Ram Menta Surgan Jandial Akash Patil Saketh Bachu Vimal K. B Balaji Krishnamurthy Vineeth N. Balasubramanian Mausoom Sarkar Chirag Agarwal Adobe Systems Indian Institute of Technology Madras Indian Institute of Technology Hyderabad Harvard University Agyeya Foundation

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suitable for a given target task without performing computationally expensive fine-tuning. Inspired by active learning techniques, we propose ACT (ACtive Transferability), a new strategy to improve the performance of transferability estimation methods, by leveraging an informative subset of the target data. By leveraging the model’s internal and output representations, we introduce two techniques – class-agnostic and class-aware – to identify informative subsets and show that ACT can be applied to any existing transferability metric to improve their performance and reliability. Our experimental results across multiple source model architectures, target datasets, and transfer learning tasks show that ACT metrics are consistently better or on par with the state-of-the-art transferability metrics.

关键词： Measurement computer vision Computational modeling conferences Transfer learning Estimation computer architecture

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Recon3D: High Quality 3D Reconstruction from a Single Image Using Generated Back-View Explicit Priors

Recon3D: High Quality 3D Reconstruction from a Single Image ...

引用

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

作者： Ruiyang Chen Mohan Yin Jiawei Shen Wei Ma Beijing University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Significant progress has been achieved in deep 3D reconstruction from a single frontal view with the aid of generative models; however, the unreliable nature of generated multi-views continues to present challenges in this domain. In this study, we propose Recon3D, a novel framework for 3D reconstruction. Recon3D exclusively utilizes a generated back view, which can be obtained more reliably through generative models based on the frontal reference image, as explicit priors. By incorporating these priors and guidance from a generative model, which is fine-tuned with Dreambooth and then enhanced with ControlNet, we effectively supervise NeRF rendering in the latent space. Subsequently, we convert the NeRF representation into an explicit point cloud and further optimize the explicit representation by referencing high-quality textured reference views. Extensive experiments demonstrate that our method achieves state-of-the-art performance in rendering novel views with superior geometry and texture quality.

关键词： Point cloud compression Geometry Solid modeling computer vision Three-dimensional displays conferences Rendering (computer graphics)

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Exploring Text-to-Motion Generation with Human Preference

Exploring Text-to-Motion Generation with Human Preference

引用

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

作者： Jenny Sheng Matthieu Lin Andrew Zhao Kevin Pruvost Yu-Hui Wen Yangguang Li Gao Huang Yong-Jin Liu Tsinghua University Beijing Jiaotong University Shanghai AI Lab

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents an exploration of preference learning in text-to-motion generation. We find that current improvements in text-to-motion generation still rely on datasets requiring expert labelers with motion capture systems. Instead, learning from human preference data does not require motion capture systems; a labeler with no expertise simply compares two generated motions. This is particularly efficient because evaluating the model’s output is easier than gathering the motion that performs a desired task (e.g. backflip). To pioneer the exploration of this paradigm, we annotate 3,528 preference pairs generated by MotionGPT, marking the first effort to investigate various algorithms for learning from preference data. In particular, our exploration highlights important design choices when using preference data. Additionally, our experimental results show that preference learning has the potential to greatly improve current text-to-motion generative models. Our code and dataset are publicly available at https://***/THU-LYJ-Lab/InstructMotion to further facilitate research in this area.

关键词： computer vision Codes conferences Computational modeling Motion capture pattern recognition

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GRASP-GCN: Graph-Shape Prioritization for Neural Architecture Search under Distribution Shifts

GRASP-GCN: Graph-Shape Prioritization for Neural Architectur...

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

作者： Sofia Casarin Oswald Lanz Sergio Escalera Free University of Bozen-Bolzano Bolzano Italy Computer Vision Center Barcelona Spain Universitat de Barcelona Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Architecture Search (NAS) methods have shown to output networks that largely outperform human-designed networks. However, conventional NAS methods have mostly tackled the single dataset scenario, incuring in a large computational cost as the procedure has to be run from scratch for every new dataset. In this work, we focus on predictor-based algorithms and propose a simple and efficient way of improving their prediction performance when dealing with data distribution shifts. We exploit the Kronecker-product on the randomly wired search-space and create a small NAS benchmark composed of networks trained over four different datasets. To improve the generalization abilities, we propose GRASP-GCN, a ranking Graph Convolutional Network that takes as additional input the shape of the layers of the neural networks. GRASP-GCN is trained with the not-at-convergence accuracies, and improves the state-of-the-art of 3.3 % for Cifar-10 and increasing moreover the generalization abilities under data distribution shift.

关键词： Training Accuracy Shape Graph convolutional networks Neural networks computer architecture Object detection

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Ensemble Spatial and Temporal vision Transformer for Action Units Detection

Ensemble Spatial and Temporal Vision Transformer for Action ...

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

作者： Ngoc Tu Vu Van Thong Huynh Trong Nghia Nguyen Soo-Hyung Kim Department of AI Convergence Chonnam National University

Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we present a simple yet efficient vision Transformer-based approach for addressing the task of Action Units (AU) detection in the context of Affective Behavior Analysis in-the-wild (ABAW) competition. We employ the Video vision Transformer(ViViT) Network to capture the temporal facial change in the video. Besides, to reduce massive size of the vision Transformers model, we replace the ViViT feature extraction layers with the CNN backbone (Regnet). Our model outperform the baseline model of ABAW 2023 challenge [8], with a notable 14% difference in result. Our team has achieved a position within the top 5 teams in the ABAW 2023 competition, scoring slightly below the top three and four teams by a narrow margin of 0.27% and 0.43%, respectively.

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The Expanding Scope of the Stability Gap: Unveiling its Presence in Joint Incremental Learning of Homogeneous Tasks

The Expanding Scope of the Stability Gap: Unveiling its Pres...

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

作者： Sandesh Kamath Albin Soutif-Cormerais Joost Van de Weijer Bogdan Raducanu Department of Computer Science Universitat Autònoma de Barcelona Computer Vision Center Barcelona

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research identified a temporary performance drop on previously learned tasks when transitioning to a new one. This drop is called the stability gap and has great consequences for continual learning: it complicates the direct employment of continually learning since the worse-case performance at task-boundaries is dramatic, it limits its potential as an energy-efficient training paradigm, and finally, the stability drop could result in a reduced final performance of the algorithm. In this paper, we show that the stability gap also occurs when applying joint incremental training of homogeneous tasks. In this scenario, the learner continues training on the same data distribution and has access to all data from previous tasks. In addition, we show that in this scenario, there exists a low-loss linear path to the next minima, but that SGD optimization does not choose this path. We perform further analysis including a finer batch-wise analysis which could provide insights towards potential solution directions.

关键词： Training Continuing education Incremental learning Employment Stability analysis Energy efficiency Trajectory

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Tracklet-based Explainable Video Anomaly Localization

Tracklet-based Explainable Video Anomaly Localization

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

作者： Ashish Singh Michael J. Jones Erik G. Learned-Miller CICS University of Massachusetts Amherst Mitsubishi Electric Research Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We take a scene-understanding approach to video anomaly localization (VAL) that leverages the rapid progress that has been made in training general deep networks for object detection, object recognition and optical flow. Our method uses each detected object’s short-term trajectory, appearance embedding, size, and location as its representation. These high-level attributes provide rich information about the object types and movements that are found in nominal video of a scene. By efficiently comparing the high-level attributes of test objects to those of normal objects, our method detects anomalous objects and anomalous movements. In addition, the human-understandable attributes used by our method can provide intuitive explanations for its decisions. We evaluate our method on many standard VAL datasets (USCD Ped1/Ped2, CUHK Avenue, ShanghaiTech and Street Scene) using spatio-temporal evaluation criteria and demonstrate new state-of-the-art accuracy.

关键词： Location awareness Training Tracking Video sequences Object detection Trajectory pattern recognition

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