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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是4171-4180 订阅

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Square Root Bundle Adjustment for Large-Scale Reconstruction

Square Root Bundle Adjustment for Large-Scale Reconstruction

引用

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

作者： Demmel, Nikolaus Sommer, Christiane Cremers, Daniel Usenko, Vladyslav Tech Univ Munich Munich Germany

ISBN: (纸本)9781665445092

We propose a new formulation for the bundle adjustment problem which relies on nullspace marginalization of landmark variables by QR decomposition. Our approach, which we call square root bundle adjustment, is algebraically equivalent to the commonly used Schur complement trick, improves the numeric stability of computations, and allows for solving large-scale bundle adjustment problems with single-precision floating-point numbers. We show in real-world experiments with the BAL datasets that even in single precision the proposed solver achieves on average equally accurate solutions compared to Schur complement solvers using double precision. It runs significantly faster, but can require larger amounts of memory on dense problems. The proposed formulation relies on simple linear algebra operations and opens the way for efficient implementations of bundle adjustment on hardware platforms optimized for single-precision linear algebra processing.

关键词： Bundle adjustment computer vision Simultaneous localization and mapping Memory management Linear algebra Libraries Hardware

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Distilling Self-Supervised vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation

Distilling Self-Supervised Vision Transformers for Weakly-Su...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Dahyun Kang Piotr Koniusz Minsu Cho Naila Murray Meta AI POSTECH Data61 ♥ CSIRO Australian National University

We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via selfattention, to produce classification and segmentation predictions through separate task heads. Our model is able to effectively learn to perform classification and segmentation in the absence of pixel-level labels during training, using only image-level labels. To do this it uses attention maps, created from tokens generated by the self-supervised ViT backbone, as pixel-level pseudo-labels. We also explore a practical setup with “mixed” supervision, where a small number of training images contains ground-truth pixel-level labels and the remaining images have only image-level labels. For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels. Experiments on Pascal-5 i and COCO-20 i demonstrate significant performance gains in a variety of supervision settings, and in particular when little-to-no pixel-level labels are available.

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Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-view Clustering

Highly Confident Local Structure Based Consensus Graph Learn...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jie Wen Chengliang Liu Gehui Xu Zhihao Wu Chao Huang Lunke Fei Yong Xu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China School of Computer Science and Technology Guangdong University of Technology Guangzhou China Pengcheng Laboratory Shenzhen China

Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness. Considering the reality of a large amount of incomplete data, in this paper, we propose a simple but effective method for incomplete multi-view clustering based on consensus graph learning, termed as HCLS_CGL. Unlike existing methods that utilize graph constructed from raw data to aid in the learning of consistent representation, our method directly learns a consensus graph across views for clustering. Specifically, we design a novel confidence graph and embed it to form a confidence structure driven consensus graph learning model. Our confidence graph is based on an intuitive similar-nearest-neighbor hypothesis, which does not require any additional information and can help the model to obtain a high-quality consensus graph for better clustering. Numerous experiments are performed to confirm the effectiveness of our method.

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DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

DANI-Net: Uncalibrated Photometric Stereo by Differentiable ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Zongrui Li Qian Zheng Boxin Shi Gang Pan Xudong Jiang School of Electrical and Electronic Engineering Nanyang Technological University Singapore The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance.

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Discrete Point-Wise Attack is Not Enough: Generalized Manifold Adversarial Attack for Face recognition

Discrete Point-Wise Attack is Not Enough: Generalized Manifo...

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conference on computer vision and pattern recognition (cvpr)

作者： Qian Li Yuxiao Hu Ye Liu Dongxiao Zhang Xin Jin Yuntian Chen Eastern Institute for Advanced Study Ningbo Zhejiang China

Classical adversarial attacks for Face recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-wise attack exhibits poor generalization against numerous unknown states of identity and can be easily defended. In this paper, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA) 1 1 https://***/tokaka22/GMAA to achieve a better attack performance by expanding the attack range. Specifically, this expansion lies on two aspects - GMAA not only expands the target to be attacked from one to many to encourage a good generalization ability for the generated adversarial examples, but it also expands the latter from discrete points to manifold by leveraging the domain knowledge that face expression change can be continuous, which enhances the attack effect as a data augmentation mechanism did. Moreover, we further design a dual supervision with local and global constraints as a minor contribution to improve the visual quality of the generated adversarial examples. We demonstrate the effectiveness of our method based on extensive experiments, and reveal that GMAA promises a semantic continuous adversarial space with a higher generalization ability and visual quality.

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Rethinking Image Super Resolution from Long-Tailed Distribution Learning Perspective

Rethinking Image Super Resolution from Long-Tailed Distribut...

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conference on computer vision and pattern recognition (cvpr)

作者： Yuanbiao Gou Peng Hu Jiancheng Lv Hongyuan Zhu Xi Peng College of Computer Science Sichuan University China Institute for Infocomm Research (I2R) A*STAR Singapore

Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little understanding is given to explain it. In this paper, we try to give a feasible answer from a machine learning perspective, i.e., the twin fitting problem caused by the long-tailed pixel distribution in natural images. With this explanation, we reformulate image super resolution (SR) as a long-tailed distribution learning problem and solve it by bridging the gaps of the problem between in low- and high-level vision tasks. As a result, we design a long-tailed distribution learning solution, that rebalances the gradients from the pixels in the low- and high-frequency region, by introducing a static and a learnable structure prior. The learned SR model achieves better balance on the fitting of the low- and high-frequency region so that the overall performance is improved. In the experiments, we evaluate the solution on four CNN- and one Transformer-based SR models w.r.t. six datasets and three tasks, and experimental results demonstrate its superiority.

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A Large-Scale Homography Benchmark

A Large-Scale Homography Benchmark

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conference on computer vision and pattern recognition (cvpr)

作者： Daniel Barath Dmytro Mishkin Michal Polic Wolfgang Förstner Jiri Matas Computer Vision and Geometry Group ETH Zurich Switzerland VRG Faculty of Electrical Engineering CTU in Prague Czech Republic HOVER Inc. CIIRC CTU in Prague Czech Republic University Bonn Germany

We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of the Pi3D dataset are diverse, e.g. training or evaluating monocular depth, surface normal estimation and image matching algorithms. The HEB dataset consists of 226 260 homographies and includes roughly 4M correspondences. The homographies link images that often undergo significant viewpoint and illumination changes. As applications of HEB, we perform a rigorous evaluation of a wide range of robust estimators and deep learning-based correspondence filtering methods, establishing the current state-of-the-art in robust homography estimation. We also evaluate the uncertainty of the SIFT orientations and scales w.r.t. the ground truth coming from the underlying homographies and provide codes for comparing uncertainty of custom detectors. The dataset is available at https://***/danini/homography-benchmark.

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Human Keypoint Change Detection for Video Violence Detection Based on Cascade Transformer 6

Human Keypoint Change Detection for Video Violence Detection...

引用

6th ieee International conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Zhou, Li Li, Wenjie Chen, Yu Liu, Hanling Yang, Mentting Liu, Zilong Chengdu Jincheng Colleage Department of Computer and Sofware Chengdu China

ISBN: (纸本)9798350325485

Human action recognition remains a challenging task in computer vision. We focus on violence detection, a subset of action recognition. In this paper, we present a video violence detection model, which introduced cascaded Transformer for human pose estimation and 3D-CNN for capturing motion present in spatial-Temporal dimension. The cascaded Transformer consist of person detecting Transformer and human keypoints detection Transformer. We trained 3D-CNN network to learn spatialtemporal features from human keypoints sequence, and output whether violence is present in video. We analyzed the influence of frame amount and human keypoints connection way on motion feature extraction. The proposed model achieves an accuracy rate of 83.2% on the RWF-2000 validation set and 90.9% on our own campus violent video dataset. © 2023 ieee.

关键词： computer vision

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Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

Neural Scene Flow Fields for Space-Time View Synthesis of Dy...

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

作者： Li, Zhengqi Niklaus, Simon Snavely, Noah Wang, Oliver Cornell Tech New York NY 10044 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural Scene Flow Fields, a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D scene motion. Our representation is optimized through a neural network to fit the observed input views. We show that our representation can be used for varieties of in-the-wild scenes, including thin structures, view-dependent effects, and complex degrees of motion. We conduct a number of experiments that demonstrate our approach significantly outperforms recent monocular view synthesis methods, and show qualitative results of space-time view synthesis on a variety of real-world videos.

关键词： Geometry Solid modeling computer vision Three-dimensional displays Dynamics Neural networks Cameras

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ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows

ArtFlow: Unbiased Image Style Transfer via Reversible Neural...

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

作者： An, Jie Huang, Siyu Song, Yibing Dou, Dejing Liu, Wei Luo, Jiebo Univ Rochester Rochester NY 14627 USA Baidu Res Beijing Peoples R China Tencent AI Lab Shenzhen Peoples R China Tencent Data Platform Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Universal style transfer retains styles from reference images in content images. While existing methods have achieved state-of-the-art style transfer performance, they are not aware of the content leak phenomenon that the image content may corrupt after several rounds of stylization process. In this paper, we propose ArtFlow to prevent content leak during universal style transfer. ArtFlow consists of reversible neural flows and an unbiased feature transfer module. It supports both forward and backward inferences and operates in a projection-transfer-reversion scheme. The forward inference projects input images into deep features, while the backward inference remaps deep features back to input images in a lossless and unbiased way. Extensive experiments demonstrate that ArtFlow achieves comparable performance to state-of-the-art style transfer methods while avoiding content leak.

关键词： computer vision Neural networks Feature extraction Inference algorithms pattern recognition

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