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Rank-One Prior: Toward Real-Time Scene Recovery

Rank-One Prior: Toward Real-Time Scene Recovery

引用

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

作者： Liu, Jun Liu, Ryan Wen Sun, Jianing Zeng, Tieyong Northeast Normal Univ Sch Math & Stat Changchun Peoples R China Northeast Normal Univ Key Lab Appl Stat MOE Changchun Peoples R China Wuhan Univ Technol Sch Nav Wuhan Peoples R China Northeast Normal Univ Jilin Natl Appl Math Ctr Changchun Peoples R China Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is O(N) where N is the size of the single image. Then we can recover the scene in real-time. Comprehensive experiments on different types of weather/imaging conditions illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.

关键词： Learning systems computer vision Visualization Imaging Estimation Training data Video surveillance

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Prior Based Human Completion

Prior Based Human Completion

引用

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

作者： Zhao, Zibo Liu, Wen Xu, Yanyu Chen, Xianing Luo, Weixin Jin, Lei Zhu, Bohui Liu, Tong Zhao, Binqiang Gao, Shenghua ShanghaiTech Univ Shanghai Peoples R China ASTAR Inst High Performance Comp Singapore Singapore Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China Taobao Hangzhou Peoples R China

ISBN: (纸本)9781665445092

We study a very challenging task, human image completion, which tries to recover the human body part with a reasonable human shape from the corrupted region. Since each human body part is unique, it is infeasible to restore the missing part by borrowing textures from other visible regions. Thus, we propose two types of learned priors to compensate for the damaged region. One is a structure prior, it uses a human parsing map to represent the human body structure. The other is a structure-texture correlation prior. It learns a structure and a texture memory bank, which encodes the common body structures and texture patterns, respectively. With the aid of these memory banks, the model could utilize the visible pattern to query and fetch a similar structure and texture pattern to introduce additional reasonable structures and textures for the corrupted region. Besides, since multiple potential human shapes are underlying the corrupted region, we propose multi-scale structure discriminators to further restore a plausible topological structure. Experiments on various large-scale benchmarks demonstrate the effectiveness of our proposed method.

关键词： Image segmentation computer vision Correlation Shape Biological system modeling Computational modeling Semantics

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Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields

Bayes' Rays: Uncertainty Quantification for Neural Radiance ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Lily Goli Cody Reading Silvia Sellán Alec Jacobson Andrea Tagliasacchi University of Toronto Simon Fraser University Adobe Research Google DeepMind

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertain-ties. Current methods to quantify them are either heuristic or computationally demanding. We introduce Bayes'Rays, a post-hoc framework to evaluate uncertainty in any pre-trained NeRF without modifying the training process. Our method establishes a volumetric uncertainty field using spa-tial perturbations and a Bayesian Laplace approximation. We derive our algorithm statistically and show its superior performance in key metrics and applications. More results available at: https://***

关键词： Training computer vision Uncertainty Three-dimensional displays Perturbation methods Octrees Neural radiance field

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Dynamic Domain Adaptation for Efficient Inference

Dynamic Domain Adaptation for Efficient Inference

引用

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

作者： Li, Shuang Zhang, JinMing Ma, Wenxuan Liu, Chi Harold Li, Wei Beijing Inst Technol Beijing Peoples R China Inceptio Tech Fremont CA USA

ISBN: (纸本)9781665445092

Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA approaches leverage complicated and powerful deep neural networks to improve the adaptation capacity and have shown remarkable success. However, they may have a lack of applicability to real-world situations such as real-time interaction, where low target inference latency is an essential requirement under limited computational budget. In this paper, we tackle the problem by proposing a dynamic domain adaptation (DDA) framework, which can simultaneously achieve efficient target inference in low-resource scenarios and inherit the favorable cross-domain generalization brought by DA. In contrast to static models, as a simple yet generic method, DDA can integrate various domain confusion constraints into any typical adaptive network, where multiple intermediate classifiers can be equipped to infer "easier" and "harder" target data dynamically. Moreover, we present two novel strategies to further boost the adaptation performance of multiple prediction exits: 1) a confidence score learning strategy to derive accurate target pseudo labels by fully exploring the prediction consistency of different classifiers;2) a class-balanced self-training strategy to explicitly adapt multi-stage classifiers from source to target without losing prediction diversity. Extensive experiments on multiple benchmarks are conducted to verify that DDA can consistently improve the adaptation performance and accelerate target inference under domain shift and limited resources scenarios.

关键词： Training Deep learning computer vision Computational modeling Benchmark testing Predictive models Real-time systems

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Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping

Multimodal Industrial Anomaly Detection by Crossmodal Featur...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Alex Costanzino Pierluigi Zama Ramirez Giuseppe Lisanti Luigi Di Stefano Department of Computer Science and Engineering (DISI) CVLAB University of Bologna Italy

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements have shown the potential of lever-aging both point clouds and images to localize anomalies. Nevertheless, their applicability in industrial manufacturing is often constrained by significant drawbacks, such as the use of memory banks, which leads to a substantial in-crease in terms of memory footprint and inference times. We propose a novel light and fast framework that learns to map features from one modality to the other on nominal samples and detect anomalies by pinpointing inconsisten-cies between observed and mapped features. Extensive ex-periments show that our approach achieves state-of-the-art detection and segmentation performance in both the stan-dard and few-shot settings on the MVTec 3D-AD dataset while achieving faster inference and occupying less memory than previous multimodal AD methods. Furthermore, we propose a layer pruning technique to improve memory and time efficiency with a marginal sacrifice in performance.

关键词： Point cloud compression computer vision Memory management Feature extraction pattern recognition Manufacturing Anomaly detection

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Dynamic LiDAR Re-Simulation Using Compositional Neural Fields

Dynamic LiDAR Re-Simulation Using Compositional Neural Field...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hanfeng Wu Xingxing Zuo Stefan Leutenegger Or Litany Konrad Schindler Shengyu Huang ETH Zurich TU Munich Technion NVIDIA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce DyNFL, a novel neural field-based approach for high-fidelity re-simulation of LiDAR scans in dynamic driving scenes. DyNFL processes LiDAR measurements from dynamic environments, accompanied by bounding boxes of moving objects, to construct an editable neural field. This field, comprising separately reconstructed static background and dynamic objects, allows users to modify viewpoints, adjust object positions, and seamlessly add or remove objects in the re-simulated scene. A key innovation of our method is the neural field composition technique, which effectively integrates reconstructed neural assets from various scenes through a ray drop test, accounting for occlusions and transparent surfaces. Our evaluation with both synthetic and real-world environments demonstrates that DyNFL substantially improves dynamic scene LiDAR simulation, offering a combination of physical fidelity and flexible editing capabilities. [project page]

关键词： Technological innovation Surface reconstruction computer vision Laser radar pattern recognition

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iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis

iMiGUE: An Identity-free Video Dataset for Micro-Gesture Und...

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

作者： Liu, Xin Shi, Henglin Chen, Haoyu Yu, Zitong Li, Xiaobai Zhao, Guoying Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Univ Oulu Oulu Finland

ISBN: (纸本)9781665445092

We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE). Different from existing public datasets, iMiGUE focuses on nonverbal body gestures without using any identity information, while the predominant researches of emotion analysis concern sensitive biometric data, like face and speech. Most importantly, iMiGUE focuses on micro-gestures, i.e., unintentional behaviors driven by inner feelings, which are different from ordinary scope of gestures from other gesture datasets which are mostly intentionally performed for illustrative purposes. Furthermore, iMiGUE is designed to evaluate the ability of models to analyze the emotional states by integrating information of recognized micro-gesture, rather than just recognizing prototypes in the sequences separately (or isolatedly). This is because the real need for emotion AI is to understand the emotional states behind gestures in a holistic way. Moreover, to counter for the challenge of imbalanced sample distribution of this dataset, an unsupervised learning method is proposed to capture latent representations from the micro-gesture sequences themselves. We systematically investigate representative methods on this dataset, and comprehensive experimental results reveal several interesting insights from the iMiGUE, e.g., micro-gesture-based analysis can promote emotion understanding. We confirm that the new iMiGUE dataset could advance studies of micro-gesture and emotion AI.

关键词： Emotion recognition computer vision Analytical models Face recognition Computational modeling Biological system modeling Prototypes

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Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Temporal Context Aggregation Network for Temporal Action Pro...

引用

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

作者： Qing, Zhiwu Su, Haisheng Gan, Weihao Wang, Dongliang Wu, Wei Wang, Xiang Qiao, Yu Yan, Junjie Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665445092

Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement. Specifically, we first design a Local-Global Temporal Encoder (LGTE), which adopts the channel grouping strategy to efficiently encode both "local and global" temporal inter-dependencies. Furthermore, both the boundary and internal context of proposals are adopted for frame-level and segment-level boundary regressions, respectively. Temporal Boundary Regressor (TBR) is designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement. Extensive experiments are conducted on three challenging datasets: HACS, ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high precision and recall. By combining with the existing action classifier, TCANet can obtain remarkable temporal action detection performance compared with other methods. Not surprisingly, the proposed TCANet won the 1st place in the cvpr 2020 - HACS challenge leaderboard on temporal action localization task.

关键词： Location awareness computer vision Benchmark testing Reliability engineering pattern recognition Proposals Task analysis

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Dense Contrastive Learning for Self-Supervised Visual Pre-Training

Dense Contrastive Learning for Self-Supervised Visual Pre-Tr...

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

作者： Wang, Xinlong Zhang, Rufeng Shen, Chunhua Kong, Tao Li, Lei Univ Adelaide Adelaide SA Australia Tongji Univ Shanghai Peoples R China ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction. To fill this gap, we aim to design an effective, dense self-supervised learning method that directly works at the level of pixels (or local features) by taking into account the correspondence between local features. We present dense contrastive learning (DenseCL), which implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images. Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation;and outperforms the state-of-the-art methods by a large margin. Specifically, over the strong MoCo-v2 baseline, our method achieves significant improvements of 2.0% AP on PASCAL VOC object detection, 1.1% AP on COCO object detection, 0.9% AP on COCO instance segmentation, 3.0% mIoU on PASCAL VOC semantic segmentation and 1.8% mIoU on Cityscapes semantic segmentation.

关键词： Learning systems Image segmentation computer vision Visualization Computational modeling Semantics Object detection

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MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

MetaCorrection: Domain-aware Meta Loss Correction for Unsupe...

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

作者： Guo, Xiaoqing Yang, Chen Li, Baopu Yuan, Yixuan City Univ Hong Kong Hong Kong Peoples R China Baidu USA Sunnyvale CA USA

ISBN: (纸本)9781665445092

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation. However;the generated pseudo labels from the model optimized on the source domain inevitably contain noise due to the domain gap. To tackle this issue, we advance a MetaCorrection framework, where a Domain-aware Meta-learning strategy is devised to benefit Loss Correction (DMLC) for UDA semantic segmentation. In particular, we model the noise distribution of pseudo labels in target domain by introducing a noise transition matrix (NTM) and construct meta data set with domain-invariant source data to guide the estimation of NTM. Through the risk minimization on the meta data set, the optimized NTM thus can correct the noisy issues in pseudo labels and enhance the generalization ability of the model on the target data. Considering the capacity gap between shallow and deep features, we fitrther employ the proposed DMLC strategy to provide matched and compatible supervision signals for different level features, thereby ensuring deep adaptation. Extensive experimental results highlight the effectiveness of our method' against existing state-of-the-art methods on three benchmarks.

关键词： Bridges Adaptation models computer vision Computational modeling Semantics Estimation Metadata

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