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Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

Wild Face Anti-Spoofing Challenge 2023: Benchmark and Result...

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2023 ieee/CVF conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Wang, Dong Guo, Jia Shao, Qiqi He, Haochi Chen, Zhian Xiao, Chuanbao Liu, Ajian Escalera, Sergio Escalante, Hugo Jair Lei, Zhen Wan, Jun Deng, Jiankang MoreDian InsightFace CASIA China University of Barcelona Spain INAOE Mexico

ISBN: (纸本)9798350302493

Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the cvpr2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface https://***/deepinsight/insightface/tree/master/challenges/cvpr23-fas-wild. © 2023 ieee.

关键词： Face recognition

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Variable Few Shot Class Incremental and OpenWorld Learning

Variable Few Shot Class Incremental and OpenWorld Learning

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

作者： Ahmad, Touqeer Dhamija, Akshay Raj Jafarzadeh, Mohsen Cruz, Steve Rabinowitz, Ryan Li, Chunchun Boult, Terrance E. Univ Colorado Vis & Secur Technol Lab Colorado Springs CO 80907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Prior work on few-shot class incremental learning has operated with an unnatural assumption: the number of ways and number of shots are assumed to be known and fixed e.g., 10-ways 5-shots, 5-ways 5-shots, etc. Hence, we refer to this setting as Fixed-Few-Shot Class Incremental Learning (FFSCIL). In practice, the pre-specified fixed number of classes and examples per class may not be available, meaning one cannot update the model. Evaluation of FSCIL approaches in such unnatural settings renders their applicability questionable for practical scenarios where such assumptions do not hold. To mitigate the limitation of FFSCIL, we propose Variable-Few-Shot Class Incremental Learning (VFSCIL) and demonstrate it with Up-to N-Ways, Up-to K-Shots class incremental learning;wherein each incremental session, a learner may have up to N classes and up to K samples per class. Consequently, conventional FFSCIL is a special case of herein introduced VFSCIL. Further, we extend VFSCIL to a more practical problem of Variable-Few-Shot OpenWorld Learning (VFSOWL), where an agent is not only required to perform incremental learning, but must detect unknown samples and enroll only those that it detects correctly. We formulate and study VFSCIL and VFSOWL on two benchmark datasets conventionally employed for FFSCIL i.e., Caltech-UCSD Birds-200-2011 (CUB200) and miniImageNet. First, to serve as a strong baseline, we extend the state-of-the-art FSCIL approach to operate in Up-to N-Ways, Up-to K-Shots class incremental and open-world settings. Then, we propose a novel but simple approach for VFSCIL/VFSOWL where we leverage the current advancements in self-supervised feature learning. Utilizing both benchmark datasets, our proposed approach outperforms the strong baseline on the conventional FFSCIL setting and newly introduced VFSCIL/VFSOWL settings.

关键词： Representation learning computer vision Codes conferences Benchmark testing Feature extraction Power capacitors

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Underwater Light Field Retention : Neural Rendering for Underwater Imaging

Underwater Light Field Retention : Neural Rendering for Unde...

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

作者： Ye, Tian Chen, Sixiang Liu, Yun Ye, Yi Chen, Erkang Li, Yuche Jimei Univ Sch Ocean Informat Engn Xiamen Peoples R China Southwest Univ Coll Artificial Intelligence Chongqing Peoples R China China Univ Petr Coll Geosci Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Underwater Image Rendering aims to generate a true-to-life underwater image from a given clean one, which could be applied to various practical applications such as underwater image enhancement, camera filter, and virtual gaming. We explore two less-touched but challenging problems in underwater image rendering, namely, i) how to render diverse underwater scenes by a single neural network? ii) how to adaptively learn the underwater light fields from natural exemplars, i,e., realistic underwater images? To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering). Specifically, UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models. Compared with existing underwater image generation methods, UWNR utilizes the natural light field to simulate the main characteristics of the underwater scene. Thus, it is able to synthesize a wide variety of underwater images from one clean image with various realistic underwater images. Extensive experiments demonstrate that our approach achieves better visual effects and quantitative metrics over previous methods. Moreover, we adopt UWNR to build an open Large Neural Rendering Underwater Dataset containing various types of water quality, dubbed LNRUD. The source code and LNRUD are available at https://***/Ephemeral182/UWNR. [GRAPHICS] .

关键词： Measurement Image synthesis conferences Neural networks Water quality Rendering (computer graphics) Visual effects

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NTIRE 2022 Image Inpainting Challenge: Report

NTIRE 2022 Image Inpainting Challenge: Report

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

作者： Romero, Andres Castillo, Angela Abril-Nova, Jose Timofte, Radu Das, Ritwik Hira, Sanchit Pan, Zhihong Zhang, Min Li, Baopu He, Dongliang Lin, Tianwei Li, Fu Wu, Chengyue Liu, Xianming Wang, Xinying Yu, Yi Yang, Jie Li, Rengang Zhao, Yaqian Guo, Zhenhua Fan, Baoyu Li, Xiaochuan Zhang, Runze Lu, Zeyu Huang, Junqin Wu, Gang Jiang, Junjun Cai, Jiayin Li, Changlin Tao, Xin Tai, Yu-Wing Zhou, Xiaoqiang Huang, Huaibo Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Los Andes Ctr Res & Format Artificial Intelligence Bogota Colombia Univ Wurzburg Wurzburg Germany Baidu Res Sunnyvale CA USA Univ Southern Calif Los Angeles CA 90089 USA Baidu Inc Dept Comp Vis Technol VIS Sunnyvale CA USA Harbin Inst Technol Harbin Peoples R China Beihang Univ Beijing Peoples R China MGTV Changchun Peoples R China Inspur Elect Informat Ind Co Ltd Jinan Peoples R China State Key Lab High End Server & Storage Technol Jinan Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781665487399

Image Inpainting has recently become an important research problem due to the rise of generative image synthesis models. While many solutions have been proposed for this problem, it is challenging to establish a testbed due to the different possible types of inpainting masks e.g., completion mask, expand mask, thick brushes mask, etc. Most inpainting solutions shine on object removal or texture synthesis, while semantic generation is still difficult to achieve. To address these issues, we introduce the first general Image Inpainting Challenge. The target is to develop solutions that can achieve a robust performance across different and challenging masks while generating compelling semantic images. The proposed challenge consists of two tracks: unsupervised image inpainting and semantically-guided image inpainting. For Track 1, the participants were provided with four datasets: FFHQ, Places, ImageNet, and WikiArt, and trained their models to perform a mask-agnostic image inpainting solution. For Track 2, FFHQ and Places only. This report gathers the description and discussion of all solutions that participated in the final stage of the challenge.

关键词： computer vision Target tracking Brushes Image synthesis conferences Computational modeling Semantics

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HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

HyperStyle: StyleGAN Inversion with HyperNetworks for Real I...

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2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

作者： Alaluf, Yuval Tov, Omer Mokady, Ron Gal, Rinon Bermano, Amit Blavatnik School of Computer Science Tel Aviv University Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, due to an inherent trade-off between reconstruction and editability: latent space regions which can accurately represent real images typically suffer from degraded semantic control. Recent work proposes to mitigate this trade-off by fine-tuning the generator to add the target image to well-behaved, editable regions of the latent space. While promising, this fine-tuning scheme is impractical for prevalent use as it requires a lengthy training phase for each new image. In this work, we introduce this approach into the realm of encoder-based inversion. We propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space. A naive modulation approach would require training a hypernetwork with over three billion parameters. Through careful network design, we reduce this to be in line with existing encoders. HyperStyle yields reconstructions comparable to those of optimization techniques with the near real-time inference capabilities of encoders. Lastly, we demonstrate HyperStyle's effectiveness on several applications beyond the inversion task, including the editing of out-of-domain images which were never seen during training. Code is available on our project page: https://***/hyperstyle/. © 2022 ieee.

关键词： Training Semantics Aerospace electronics Generators Robustness Real-time systems pattern recognition

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Proceedings - 2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

Proceedings - 2022 IEEE/CVF Conference on Computer Vision an...

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2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

ISBN: (纸本)9781665469463

The proceedings contain 2072 papers. The topics discussed include: clipped hyperbolic classifiers are super-hyperbolic classifiers;efficient deep embedded subspace clustering;noise is also useful: negative correlation-steered latent contrastive learning;active learning for open-set annotation;understanding and increasing efficiency of Frank-Wolfe adversarial training;robust optimization as data augmentation for large-scale graphs;a re-balancing strategy for class-imbalanced classification based on instance difficulty;the devil is in the margin: margin-based label smoothing for network calibration;towards better plasticity-stability trade-off in incremental learning: a simple linear connector;learning Bayesian sparse networks with full experience replay for continual learning;a variational Bayesian method for similarity learning in non-rigid image registration;learning to learn by jointly optimizing neural architecture and weights;learning to prompt for continual learning;multi-frame self-supervised depth with transformers;and rethinking Bayesian deep learning methods for semi-supervised volumetric medical image segmentation.

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E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

E2EC: An End-to-End Contour-based Method for High-Quality Hi...

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2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

作者： Zhang, Tao Wei, Shiqing Ji, Shunping Wuhan University Wuhan China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Contour-based instance segmentation methods have developed rapidly recently but feature rough and hand-crafted front-end contour initialization, which restricts the model performance, and an empirical and fixed backend predicted-label vertex pairing, which contributes to the learning difficulty. In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation. Firstly, E2EC applies a novel learnable contour initialization architecture instead of hand-crafted contour initialization. This consists of a contour initialization module for constructing more explicit learning goals and a global contour deformation module for taking advantage of all of the vertices' features better. Secondly, we propose a novel label sampling scheme, named multi-direction alignment, to reduce the learning difficulty. Thirdly, to improve the quality of the boundary details, we dynamically match the most appropriate predicted-ground truth vertex pairs and propose the corresponding loss function named dynamic matching loss. The experiments showed that E2EC can achieve a state-of-the-art performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for use in real-time applications, with an inference speed of 36 fps for $512\times 512$ images on an NVIDIA A6000 GPU. Code will be released at https://***/zhang-tao-whu/e2ec. © 2022 ieee.

关键词： Image segmentation Shape Semantics Graphics processing units computer architecture Detectors Predictive models

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NTIRE 2023 Video Colorization Challenge

NTIRE 2023 Video Colorization Challenge

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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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NTIRE 2022 Spectral Demosaicing Challenge and Data Set

NTIRE 2022 Spectral Demosaicing Challenge and Data Set

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

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Wu, Yaqi Wu, Xun Fan, Zhihao Xia, Chenjie Zhang, Feng Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Zhang, Mingwei Feng, Kai Zhang, Xun Yao, Jiaxin Zhao, Yongqiang Ma, Suina He, Fan Dong, Yangyang Yu, Shufang Qiu, Difa Liu, Jinhui Bi, Mengzhao Song, Beibei Sun, WenFang Zheng, Jiesi Zhao, Bowen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Kong, Xiangyu Yu, Jingbo Xue, Yuanyang Xie, Zheng Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Swiss Fed Inst Technol Computat Vision Lab Zurich Switzerland Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Harbin Inst Technol Harbin 150001 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Xiaomi Inc Beijing Peoples R China Northwestern Polytech Univ Xian Peoples R China Changan Univ Xian Peoples R China Xidian Univ Xian Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mech Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents the first challenge on demosaicing of natural spectral images for snapshot hyperspectral imaging systems (HIS) which utilize a multi-spectral filer array (MSFA), i.e., the recovery of whole-scene hyperspectral information from spatially sub-sampled hyperspectral information. This challenge expands the "ARAD_1K" data set to a first-of-its-kind large-scale data set for multi-spectral filter array demosaicing of natural scenes containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated MSFA images simulating capture by a known calibrated snapshot mosaic hyperspectral camera. The challenge was attended by 157 teams, with 29 teams competing in the final testing phase, 7 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in multi-spectral filter array demosaicing of natural images.

关键词： Performance evaluation Training Runtime Neural networks Cameras Real-time systems pattern recognition

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BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

BasicVSR++: Improving Video Super-Resolution with Enhanced P...

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2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

作者： Chan, Kelvin C.K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Nanyang Technological University S-Lab Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature alignment to effectively exploit information from the entire input video. In this study, we redesign BasicVsr by proposing second-order grid propagation and flow-guided deformable alignment. We show that by empowering the re-current framework with enhanced propagation and align-ment, one can exploit spatiotemporal information across misaligned video frames more effectively. The new components lead to an improved performance under a simi-lar computational constraint. In particular, our model Ba-sicVSR++ surpasses BasicVSR by a significant 0.82 dB in PSNR with similar number of parameters. BasicVSR++ is generalizable to other video restoration tasks, and obtains three champions and one first runner-up in NTIRE 2021 video restoration challenge. © 2022 ieee.

关键词： computer vision Computational modeling Superresolution Spatiotemporal phenomena pattern recognition Task analysis

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