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All-in-Focus Imaging from Event Focal Stack

All-in-Focus Imaging from Event Focal Stack

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conference on computer vision and pattern recognition (CVPR)

作者： Hanyue Lou Minggui Teng Yixin Yang Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus and deblurring problem. In this paper, to restore an all-in-focus image, we propose the event focal stack which is defined as event streams captured during a continuous focal sweep. Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack. Guided by the neighbouring events around the selected timestamps, we can merge the focal stack with proper weights and restore a sharp all-in-focus image. Experimental results on both synthetic and real datasets show superior performance over state-of-the-art methods.

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DegAE: A New Pretraining Paradigm for Low-Level vision

DegAE: A New Pretraining Paradigm for Low-Level Vision

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conference on computer vision and pattern recognition (CVPR)

作者： Yihao Liu Jingwen He Jinjin Gu Xiangtao Kong Yu Qiao Chao Dong Shanghai Artificial Intelligence Laboratory ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences The University of Sydney

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acqui-sition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (De-gAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tai-lored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.

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SemiCVT: Semi-Supervised Convolutional vision Transformer for Semantic Segmentation

SemiCVT: Semi-Supervised Convolutional Vision Transformer fo...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Huimin Huang Shiao Xie Lanfen Lin Ruofeng Tong Yen-Wei Chen Yuexiang Li Hong Wang Yawen Huang Yefeng Zheng Zhejiang University Zhejiang Lab Ritsumeikan University Tencent Jarvis Lab

Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples. These successes concentrate on the pixel-wise consistency by using convolutional neural networks (CNNs) but fail to address both global learning capability and class-level features for unlabeled data. Recent works raise a new trend that Transformer achieves superior performance on the entire feature map in various tasks. In this paper, we unify the current dominant Mean-Teacher approaches by reconciling intra-model and inter-model properties for semi-supervised segmentation to produce a novel algorithm, SemiCVT, that absorbs the quintessence of CNNs and Transformer in a comprehensive way. Specifically, we first design a parallel CNN-Transformer architecture (CVT) with introducing an intra-model local-global interaction schema (LGI) in Fourier domain for full integration. The inter-model class-wise consistency is further presented to complement the class-level statistics of CNNs and Transformer in a cross-teaching manner. Extensive empirical evidence shows that SemiCVT yields consistent improvements over the state-of-the-art methods in two public benchmarks.

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Evolved Part Masking for Self-Supervised Learning

Evolved Part Masking for Self-Supervised Learning

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conference on computer vision and pattern recognition (CVPR)

作者： Zhanzhou Feng Shiliang Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Peng Cheng Laboratory

Existing Masked Image Modeling methods apply fixed mask patterns to guide the self-supervised training. As those patterns resort to different criteria to mask local regions, sticking to a fixed pattern leads to limited vision cues modeling capability. This paper proposes an evolved part-based masking to pursue more general visual cues modeling in self-supervised learning. Our method is based on an adaptive part partition module, which leverages the vision model being trained to construct a part graph, and partitions parts with graph cut. The accuracy of partitioned parts is on par with the capability of the pretrained model, leading to evolved mask patterns at different training stages. It generates simple patterns at the initial training stage to learn low-level visual cues, which hence evolves to eliminate accurate object parts to reinforce the learning of object semantics and contexts. Our method does not require extra pretrained models or annotations, and effectively ensures the training efficiency by evolving the training difficulty. Experiment results show that it substantially boosts the performance on various tasks including image classification, object detection, and semantic segmentation. For example, it outperforms the recent MAE by 0.69% on imageNet-1K classification and 1.61% on ADE20K segmentation with the same training epochs.

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Complementary Intrinsics from Neural Radiance Fields and CNNs for Outdoor Scene Relighting

Complementary Intrinsics from Neural Radiance Fields and CNN...

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conference on computer vision and pattern recognition (CVPR)

作者： Siqi Yang Xuanning Cui Yongjie Zhu Jiajun Tang Si Li Zhaofei Yu Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Institute for Artificial Intelligence Peking University AI Innovation Center School of Computer Science Peking University School of Artificial Intelligence Beijing University of Posts and Telecommunications

Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multiview stereo. With neural radiance fields (NeRF), editing the appearance code could produce more realistic results without interpreting the outdoor scene image formation explicitly. This paper proposes to complement the intrinsic estimation from volume rendering using NeRF and from inversing the photometric image formation model using convolutional neural networks (CNNs). The former produces richer and more reliable pseudo labels (cast shadows and sky appearances in addition to albedo and normal) for training the latter to predict interpretable and editable lighting parameters via a single-image prediction pipeline. We demonstrate the advantages of our method for both intrinsic image decomposition and relighting for various real outdoor scenes.

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TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in vision-Centric Autonomous Driving

TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Jo...

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conference on computer vision and pattern recognition (CVPR)

作者： Shaoheng Fang Zi Wang Yiqi Zhong Junhao Ge Siheng Chen Cooperative Medianet Innovation Center Shanghai Jiao Tong University Department of Computer Science University of Southern California Shanghai AI Laboratory

vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a critical challenge to synchronize features obtained at multiple camera views and timestamps due to inevitable geometric distortions and further exploit those spatial-temporal features. To address this issue, we propose a temporal bird's-eye-view pyramid transformer (TBP-Former) for vision-centric PnP; which includes two novel designs. First, a pose-synchronized BEV encoder is proposed to map raw image inputs with any camera pose at any time to a shared and synchronized BEV space for better spatial-temporal synchronization. Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial priors. Extensive experiments on nuScenes dataset show that our proposed framework overall outperforms all state-of-the-art vision-based prediction methods. Code is available at: https://***/MediaBrain-SJTU/TBP-Former

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Video Action Detection: Analysing Limitations and Challenges

Video Action Detection: Analysing Limitations and Challenges

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

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset? Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection. The task aims to spatio-temporally localize an actor and assign a relevant action class. We first analyze the existing datasets on video action detection and discuss their limitations. Next, we propose a new dataset, Multi Actor Multi Action (MAMA) which overcomes these limitations and is more suitable for real world applications. In addition, we perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect. This reveals if the actions in these datasets really need the motion information of an actor, or whether they predict the occurrence of an action even by looking at a single frame. Finally, we investigate the widely held assumptions on the importance of temporal ordering: is temporal ordering important for detecting these actions? Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.

关键词： pattern recognition conferences computer vision

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Mask3D: Pretraining 2D vision Transformers by Learning Masked 3D Priors

Mask3D: Pretraining 2D Vision Transformers by Learning Maske...

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conference on computer vision and pattern recognition (CVPR)

作者： Ji Hou Xiaoliang Dai Zijian He Angela Dai Matthias Nieβner Meta Reality Labs Technical University of Munich

Current popular backbones in computer vision, such as vision Transformers (ViT) and ResNets are trained to per-ceive the world from 2D images. However, to more effectively understand 3D structural priors in 2D backbones, we propose Mask3D to leverage existing large-scale RGB-D data in a self-supervised pretraining to embed these 3D priors into 2D learned feature representations. In contrast to traditional 3D contrastive learning paradigms requiring 3D reconstructions or multi-view correspondences, our approach is simple: we formulate a pre-text reconstruction task by masking RGB and depth patches in individual RGB-D frames. We demonstrate the Mask3D is particularly effective in embedding 3D priors into the powerful 2D ViT backbone, enabling improved representation learning for various scene understanding tasks, such as semantic segmentation, instance segmentation and object detection. Experiments show that Mask3D notably outperforms existing self-supervised 3D pretraining approaches on ScanNet, NYUv2, and Cityscapes image understanding tasks, with an improvement of +6.5% mIoU against the state-of-the-art Pri3D on ScanNet image semantic segmentation.

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High-Fidelity Guided Image Synthesis with Latent Diffusion Models

High-Fidelity Guided Image Synthesis with Latent Diffusion M...

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conference on computer vision and pattern recognition (CVPR)

作者： Jaskirat Singh Stephen Gould Liang Zheng The Australian National University Australian Centre for Robotic Vision

Controllable image synthesis with user scribbles has gained huge public interest with the recent advent of text-conditioned latent diffusion models. The user scribbles control the color composition while the text prompt provides control over the overall image semantics. However, we note that prior works in this direction suffer from an intrinsic domain shift problem wherein the generated outputs often lack details and resemble simplistic representations of the target domain. In this paper, we propose a novel guided image synthesis framework, which addresses this problem by modelling the output image as the solution of a constrained optimization problem. We show that while computing an exact solution to the optimization is infeasible, an approximation of the same can be achieved while just requiring a single pass of the reverse diffusion process. Additionally, we show that by simply defining a cross-attention based correspondence between the input text tokens and the user stroke-painting, the user is also able to control the semantics of different painted regions without requiring any conditional training or finetuning. Human user study results show that the proposed approach outperforms the previous state-of-the-art by over 85.32% on the overall user satisfaction scores. Project page for our paper is available at https://***/gradop.

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NTIRE 2021 Challenge on Video Super-Resolution

NTIRE 2021 Challenge on Video Super-Resolution

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

作者： Son, Sanghyun Lee, Suyoung Nah, Seungjun Timofte, Radu Lee, Kyoung Mu Chan, Kelvin C. K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Jiang, Boyuan Lin, Chuming Dong, Yuchun Luo, Donghao Chu, Wenqing Ji, Xiaozhong Yang, Siqian Tai, Ying Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Chengpeng Chu, Xiaojie Zhang, Jie Lu, Xin Chen, Liangyu Lin, Jing Du, Guodong Hao, Jia Zou, Xueyi Zhang, Qi Jiang, Lielin Li, Xin Zheng, He Liu, Fanglong He, Dongliang Li, Fu Dang, Qingqing Yi, Peng Wang, Zhongyuan Jiang, Kui Jiang, Junjun Ma, Jiayi Chen, Yuxiang Wang, Yutong Liu, Ting Sun, Qichao Liang, Huanwei Li, Yiming Li, Zekun Ruan, Zhubo Shang, Fanjie Guo, Chen Li, Haining Luo, Renjun Shen, Longjie Zafirouli, Kassiani Karageorgos, Konstantinos Konstantoudakis, Konstantinos Dimou, Anastasios Daras, Petros Song, Xiaowei Zhuo, Xu Liu, Hanxi Guo, Mengxi Li, Junlin Li, Yu Zhu, Ye Wang, Qing Zhao, Shijie Sun, Xiaopeng Zhan, Gen Xie, Tangxin Jia, Yu Lu, Yunhua Zhang, Wenhao Sun, Mengdi Michelini, Pablo Navarrete Zhang, Xueheng Jiang, Hao Chen, Zhiyu Chen, Li Xiong, Zhiwei Xiao, Zeyu Xu, Ruikang Cheng, Zhen Fu, Xueyang Song, Fenglong Luo, Zhipeng Yao, Yuehan Dutta, Saikat Shah, Nisarg A. Das, Sourya Dipta Zhao, Peng Shi, Yukai Liu, Hongying Shang, Fanhua Liu, Yuanyuan Chen, Fei Yu, Fangxu Gao, Ruisheng Bai, Yixin Heo, Jeonghwan Yue, Shijie Li, Chenghua Li, Jinjing Zheng, Qian Gang, Ruipeng Song, Ruixia Wee, Seungwoo Jeong, Jechang Li, Chen Wen, Geyingjie Chai, Xinning Song, Li SNU ASRI Dept ECE Seoul South Korea Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Nanyang Technol Univ S Lab Singapore Singapore Tencent YouTu Lab Shenzhen Peoples R China East China Normal Univ Shanghai Peoples R China Megvii Beijing Peoples R China Fudan Univ Shanghai Peoples R China Huawei Noahs Ark Lab Shanghai Peoples R China HiSilicon Shanghai Technol Co Ltd Shanghai Peoples R China Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China Wuhan Univ Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Meitu Inc MTlab Xiamen Peoples R China Xidian Univ Xian Peoples R China ITI CERTH Visual Comp Lab VCL Patras Greece Southeast Univ Nanjing Peoples R China Tencent PCG Appl Res Ctr ARC Shenzhen Peoples R China South China Univ Technol Guangzhou Peoples R China ByteDance Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China China Elect Technol Cyber Secur Co Ltd Beijing Peoples R China BOE Technol Grp Co Ltd AIoT R&D Ctr Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Sci & Technol China Chengdu Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Indian Inst Technol Madras Madras Tamil Nadu India Indian Inst Technol Jodhpur Jodhpur Rajasthan India Jadavpur Univ Kolkata India Hanyang Univ Seoul South Korea NCUT Beijing Peoples R China CASIA Beijing Peoples R China CUC Beijing Peoples R China NRTA Beijing Peoples R China

ISBN: (纸本)9781665448994

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.

关键词： computer vision Casting conferences Superresolution Focusing pattern recognition Image restoration

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