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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是181-190 订阅

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PREDATOR: Registration of 3D Point Clouds with Low Overlap

PREDATOR: Registration of 3D Point Clouds with Low Overlap

引用

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

作者： Huang, Shengyu Gojcic, Zan Usvyatsov, Mikhail Wieser, Andreas Schindler, Konrad Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665445092

We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region. Different from previous work, our model is specifically designed to handle (also) point-cloud pairs with low overlap. Its key novelty is an overlap-attention block for early information exchange between the latent encodings of the two point clouds. In this way the subsequent decoding of the latent representations into per-point features is conditioned on the respective other point cloud, and thus can predict which points are not only salient, but also lie in the overlap region between the two point clouds. The ability to focus on points that are relevant for matching greatly improves performance: PREDATOR raises the rate of successful registrations by more than 20% in the low-overlap scenario, and also sets a new state of the art for the 3DMatch benchmark with 89% registration recall.

关键词： Convolutional codes Solid modeling computer vision Three-dimensional displays Image matching computer architecture Encoding

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Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning

Continual Adaptation of Visual Representations via Domain Ra...

引用

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

作者： Volpi, Riccardo Larlus, Diane Rogez, Gregory NAVER LABS Europe Meylan France

ISBN: (纸本)9781665445092

Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature-the well-known catastrophic forgetting issue. In particular, when a model consecutively learns from different visual domains, it tends to forget the past domains in favor of the most recent ones. In this context, we show that one way to learn models that are inherently more robust against forgetting is domain randomization-for vision tasks, randomizing the current domain's distribution with heavy image manipulations. Building on this result, we devise a meta-learning strategy where a regularizer explicitly penalizes any loss associated with transferring the model from the current domain to different "auxiliary" meta-domains, while also easing adaptation to them. Such meta-domains are also generated through randomized image manipulations. We empirically demonstrate in a variety of experiments-spanning from classification to semantic segmentation-that our approach results in models that are less prone to catastrophic forgetting when transferred to new domains.

关键词： Visualization Adaptation models Image segmentation computer vision Computational modeling Semantics pattern recognition

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Square Root Bundle Adjustment for Large-Scale Reconstruction

Square Root Bundle Adjustment for Large-Scale Reconstruction

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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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SuperMix: Supervising the Mixing Data Augmentation

SuperMix: Supervising the Mixing Data Augmentation

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

作者： Dabouei, Ali Soleymani, Sobhan Taherkhani, Fariborz Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781665445092

This paper presents a supervised mixing augmentation method termed SuperMix, which exploits the salient regions within input images to construct mixed training samples. SuperMix is designed to obtain mixed images rich in visual features and complying with realistic image priors. To enhance the efficiency of the algorithm, we develop a variant of the Newton iterative method, 65x faster than gradient descent on this problem. We validate the effectiveness of SuperMix through extensive evaluations and ablation studies on two tasks of object classification and knowledge distillation. On the classification task, SuperMix provides comparable performance to the advanced augmentation methods, such as AutoAugment and RandAugment. In particular, combining SuperMix with RandAugment achieves 78.2% top-1 accuracy on ImageNet with ResNet50. On the distillation task, solely classifying images mixed using the teacher's knowledge achieves comparable performance to the state-of-the-art distillation methods. Furthermore, on average, incorporating mixed images into the distillation objective improves the performance by 3.4% and 3.1% on CIFAR-100 and ImageNet, respectively. The code is available at https://***/alldbi/SuperMix.

关键词： Training Visualization computer vision Codes Computational modeling pattern recognition Classification algorithms

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End-to-End Human Pose and Mesh Reconstruction with Transformers

End-to-End Human Pose and Mesh Reconstruction with Transform...

引用

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

作者： Lin, Kevin Wang, Lijuan Liu, Zicheng Microsoft Albuquerque NM 87108 USA

ISBN: (纸本)9781665445092

We present a new method, called MEsh TRansfOrmer (METRO), to reconstruct 3D human pose and mesh vertices from a single image. Our method uses a transformer encoder to jointly model vertex-vertex and vertex-joint interactions, and outputs 3D joint coordinates and mesh vertices simultaneously. Compared to existing techniques that regress pose and shape parameters, METRO does not rely on any parametric mesh models like SMPL, thus it can be easily extended to other objects such as hands. We further relax the mesh topology and allow the transformer self-attention mechanism to freely attend between any two vertices, making it possible to learn non-local relationships among mesh vertices and joints. With the proposed masked vertex modeling, our method is more robust and effective in handling challenging situations like partial occlusions. METRO generates new state-of-the-art results for human mesh reconstruction on the public Human3.6M and 3DPW datasets. Moreover, we demonstrate the generalizability of METRO to 3D hand reconstruction in the wild, outperforming existing state-of-the-art methods on FreiHAND dataset.

关键词： Solid modeling computer vision Three-dimensional displays Shape Transformers Topology pattern recognition

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Repopulating Street Scenes

Repopulating Street Scenes

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

作者： Wang, Yifan Liu, Andrew Tucker, Richard Wu, Jiajun Curless, Brian L. Seitz, Steven M. Snavely, Noah Univ Washington Seattle WA 98195 USA Google Res Mountain View CA USA Stanford Univ Stanford CA 94305 USA Google Mountain View CA USA

ISBN: (纸本)9781665445092

We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. Applications of this method include anonymizing images to enhance privacy, generating data augmentations for perception tasks like autonomous driving, and composing scenes to achieve a certain ambiance, such as empty streets in the early morning. At a technical level, our work has three primary contributions: (1) a method for clearing images of objects, (2) a method for estimating sun direction from a single image, and (3) a way to compose objects in scenes that respects scene geometry and illumination. Each component is learned from data with minimal ground truth annotations, by making creative use of large-numbers of short image bursts of street scenes. We demonstrate convincing results on a range of street scenes and illustrate potential applications.

关键词： Geometry Data privacy computer vision Annotations Lighting pattern recognition Task analysis

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3D Human Action Representation Learning via Cross-View Consistency Pursuit

3D Human Action Representation Learning via Cross-View Consi...

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

作者： Li, Linguo Wang, Minsi Ni, Bingbing Wang, Hang Yang, Jiancheng Zhang, Wenjun Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multiview complementary supervision signal. CrosSCLR consists of both single-view contrastive learning (Skeleton-CLR) and cross-view consistent knowledge mining (CVC-KM) modules, integrated in a collaborative learning manner. It is noted that CVC-KM works in such a way that high-confidence positive/negative samples and their distributions are exchanged among views according to their embedding similarity, ensuring cross-view consistency in terms of contrastive context, i.e., similar distributions. Extensive experiments show that CrosSCLR achieves remarkable action recognition results on NTU-60 and NTU-120 dmasets under unsupervised settings, with observed higherquality action representations. Our code is available at https://***/Linguoli/CrosSCLR.

关键词： computer vision Three-dimensional displays Codes Collaborative work pattern recognition

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Few-Shot Transformation of Common Actions into Time and Space

Few-Shot Transformation of Common Actions into Time and Spac...

引用

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

作者： Yang, Pengwan Mettes, Pascal Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665445092

This paper introduces the task of few-shot common action localization in time and space. Given a few trimmed support videos containing the same but unknown action, we strive for spatio-temporal localization of that action in a long untrimmed query video. We do not require any class labels, interval bounds, or bounding boxes. To address this challenging task, we introduce a novel few-shot transformer architecture with a dedicated encoder-decoder structure optimized for joint commonality learning and localization prediction, without the need for proposals. Experiments on reorganizations of the AVA and UCF101-24 datasets show the effectiveness of our approach for few-shot common action localization, even when the support videos are noisy. Although we are not specifically designed for common localization in time only, we also compare favorably against the fewshot and one-shot state-of-the-art in this setting. Lastly, we demonstrate that the few-shot transformer is easily extended to common action localization per pixel.

关键词： Location awareness computer vision computer architecture Transformers pattern recognition Noise measurement Proposals

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Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings

Privacy-Preserving Image Features via Adversarial Affine Sub...

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

作者： Dusmanu, Mihai Schoenberger, Johannes L. Sinha, Sudipta N. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Menlo Pk CA USA

ISBN: (纸本)9781665445092

Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by embedding it within an affine subspace containing the original feature as well as adversarial feature samples. Feature matching on the privacy-preserving representation is enabled based on the notion of subspace-to-subspace distance. We experimentally demonstrate the effectiveness of our method and its high practical relevance for the applications of visual localization and mapping as well as face authentication. Compared to the original features, our approach makes it significantly more difficult for an adversary to recover private information.

关键词： Location awareness Visualization computer vision Privacy Face recognition Authentication Rendering (computer graphics)

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Learning to Warp for Style Transfer

Learning to Warp for Style Transfer

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

作者： Liu, Xiao-Chang Yang, Yong-Liang Hall, Peter Univ Bath Bath Avon England

ISBN: (纸本)9781665445092

Since its inception in 2015, Style Transfer has focused on texturing a content image using an art exemplar. Recently, the geometric changes that artists make have been acknowledged as an important component of style [42, 55, 62, 63]. Our contribution is to propose a neural network that, uniquely, learns a mapping from a 4D array of inter-feature distances to a non parametric 2D warp field. The system is generic in not being limited by semantic class, a single learned model will suffice;all examples in this paper are output from one model. Our approach combines the benefits of the high speed of Liu et al. [42] with the non-parametric warping of Kim et al. [55]. Furthermore, our system extends the normal NST paradigm: although it can be used with a single exemplar, we also allow two style exemplars: one for texture and another for geometry. This supports far greater flexibility in use cases than single exemplars can provide.

关键词： Geometry computer vision Art Semantics Neural networks pattern recognition Strain

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