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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71-80 订阅

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DexYCB: A Benchmark for Capturing Hand Grasping of Objects

DexYCB: A Benchmark for Capturing Hand Grasping of Objects

引用

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

作者： Chao, Yu-Wei Yang, Wei Xiang, Yu Molchanov, Pavlo Handa, Ankur Tremblay, Jonathan Narang, Yashraj S. Van Wyk, Karl Iqbal, Umar Birchfield, Stan Kautz, Jan Fox, Dieter NVIDIA Santa Clara CA 95050 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough benchmark of state-of-the-art approaches on three relevant tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand pose estimation. Finally, we evaluate a new robotics-relevant task: generating safe robot grasps in human-to-robot object handover.(1)

关键词： computer vision Three-dimensional displays Pose estimation Grasping Benchmark testing Handover Drives

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引用

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

作者： Wray, Michael Doughty, Hazel Damen, Dima Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (纸本)9781665445092

Current video retrieval efforts all found their evaluation on an instance-based assumption, that only a single caption is relevant to a query video and vice versa. We demonstrate that this assumption results in performance comparisons often not indicative of models' retrieval capabilities. We propose a move to semantic similarity video retrieval, where (i) multiple videos/captions can be deemed equally relevant, and their relative ranking does not affect a method's reported performance and (ii) retrieved videos/captions are ranked by their similarity to a query. We propose several proxies to estimate semantic similarities in large-scale retrieval datasets, without additional annotations. Our analysis is performed on three commonly used video retrieval datasets (MSR-VTT, YouCook2 and EPIC-KITCHENS).

关键词： Training computer vision Protocols Annotations Computational modeling Semantics Benchmark testing

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SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place recognition

SOE-Net: A Self-Attention and Orientation Encoding Network f...

引用

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

作者： Xia, Yan Xu, Yusheng Li, Shuang Wang, Rui Du, Juan Cremers, Daniel Stilla, Uwe Tech Univ Munich Munich Germany Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors. Local information of each point from eight orientations is captured in a PointOE module, whereas long-range feature dependencies among local descriptors are captured with a self-attention unit. Moreover, we propose a novel loss function called Hard Positive Hard Negative quadruplet loss (HPHN quadruplet), that achieves better performance than the commonly used metric learning loss. Experiments on various benchmark datasets demonstrate superior performance of the proposed network over the current state-of-the-art approaches.

关键词： Measurement computer vision Codes Benchmark testing Encoding pattern recognition

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Improving Panoptic Segmentation at All Scales

Improving Panoptic Segmentation at All Scales

引用

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

作者： Porzi, Lorenzo Bulo, Samuel Rota Kontschieder, Peter Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

Crop-based training strategies decouple training resolution from GPU memory consumption, allowing the use of large-capacity panoptic segmentation networks on multi-megapixel images. Using crops, however, can introduce a bias towards truncating or missing large objects. To address this, we propose a novel crop-aware bounding box regression loss (CABS loss), which promotes predictions to be consistent with the visible parts of the cropped objects, while not over-penalizing them for extending outside of the crop. We further introduce a novel data sampling and augmentation strategy which improves generalization across scales by counteracting the imbalanced distribution of object sizes. Combining these two contributions with a carefully designed, top-down panoptic segmentation architecture, we obtain new state-of-the-art results on the challenging Mapillary Vistas (MVD), Indian Driving and Cityscapes datasets, surpassing the previously best approach on MVD by +4.5% PQ and +5.2% mAP.

关键词： Training Measurement Image segmentation computer vision Image resolution Memory management Crops

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Improving Calibration for Long-Tailed recognition

Improving Calibration for Long-Tailed Recognition

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

作者： Zhong, Zhisheng Cui, Jiequan Liu, Shu Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Deep neural networks may perform poorly when training datasets are heavily class-imbalanced. Recently, two-stage methods decouple representation learning and classifier learning to improve performance. But there is still the vital issue of miscalibration. To address it, we design two methods to improve calibration and performance in such scenarios. Motivated by the fact that predicted probability distributions of classes are highly related to the numbers of class instances, we propose label-aware smoothing to deal with different degrees of over-confidence for classes and improve classifier learning. For dataset bias between these two stages due to different samplers, we further propose shifted batch normalization in the decoupling framework. Our proposed methods set new records on multiple popular long-tailed recognition benchmark datasets, including CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, Places-LT, and iNaturalist 2018.

关键词： Training Deep learning computer vision Smoothing methods Image recognition Design methodology Benchmark testing

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MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection

MultiLink: Multi-class Structure Recovery via Agglomerative ...

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

作者： Magri, Luca Leveni, Filippo Boracchi, Giacomo Politecn Milan DEIB Milan Italy

ISBN: (纸本)9781665445092

We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defined by a mixture of underlying parametric models (e.g. planes and cylinders, homographies and fundamental matrices), and we tackle the robust fitting problem by preference analysis and clustering. We present a new algorithm, termed MultiLink, that simultaneously deals with multiple classes of models. MultiLink combines on-the-fly model fitting and model selection in a novel linkage scheme that determines whether two clusters are to be merged. The resulting method features many practical advantages with respect to methods based on preference analysis, being faster, less sensitive to the inlier threshold, and able to compensate limitations deriving from hypotheses sampling. Experiments on several public datasets demonstrate that MultiLink favourably compares with state of the art alternatives, both in multi-class and single-class problems. Code is publicly made available for download(1).

关键词： Couplings computer vision Codes Fitting Clustering algorithms Tools Data models

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Quantum Permutation Synchronization

Quantum Permutation Synchronization

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

作者： Birdal, Tolga Golyanik, Vladislav Theobalt, Christian Guibas, Leonidas Stanford Univ Stanford CA 94305 USA Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9781665445092

We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. In particular, we focus on permutation synchronization which involves solving a non-convex optimization problem in discrete variables. We start by formulating synchronization into a quadratic unconstrained binary optimization problem (QUBO). While such formulation respects the binary nature of the problem, ensuring that the result is a set of permutations requires extra care. Hence, we: (i) show how to insert permutation constraints into a QUBO problem and (ii) solve the constrained QUBO problem on the current generation of the adiabatic quantum computers D-Wave. Thanks to the quantum annealing, we guarantee global optimality with high probability while sampling the energy landscape to yield confidence estimates. Our proofof-concepts realization on the adiabatic D-Wave computer demonstrates that quantum machines offer a promising way to solve the prevalent yet difficult synchronization problems.

关键词： computer vision Quantum algorithm Adiabatic Hardware pattern recognition Synchronization Optimization

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Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency

Self-Supervised Video GANs: Learning for Appearance Consiste...

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

作者： Hyun, Sangeek Kim, Jihwan Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea

ISBN: (纸本)9781665445092

A video can be represented by the composition of appearance and motion. Appearance (or content) expresses the information invariant throughout time, and motion describes the time-variant movement. Here, we propose self-supervised approaches for video Generative Adversarial Networks (GANs) to achieve the appearance consistency and motion coherency in videos. Specifically, the dual discriminators for image and video individually learn to solve their own pretext tasks;appearance contrastive learning and temporal structure puzzle. The proposed tasks enable the discriminators to learn representations of appearance and temporal context, and force the generator to synthesize videos with consistent appearance and natural flow of motions. Extensive experiments in facial expression and human action public benchmarks show that our method outperforms the state-of-the-art video GANs. Moreover, consistent improvements regardless of the architecture of video GANs confirm that our framework is generic.

关键词： computer vision Force computer architecture Benchmark testing Generative adversarial networks Generators pattern recognition

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Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph

Hierarchical and Partially Observable Goal-driven Policy Lea...

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

作者： Ye, Xin Yang, Yezhou Arizona State Univ Sch Comp Informat & Decis Syst Engn Act Percept Grp Tempe AZ 85281 USA

ISBN: (纸本)9781665445092

We present a novel two-layer hierarchical reinforcement learning approach equipped with a Goals Relational Graph (GRG) for tackling the partially observable goal-driven task, such as goal-driven visual navigation. Our GRG captures the underlying relations of all goals in the goal space through a Dirichlet-categorical process that facilitates: 1) the highlevel network raising a sub-goal towards achieving a designated final goal;2) the low-level network towards an optimal policy;and 3) the overall system generalizing unseen environments and goals. We evaluate our approach with two settings of partially observable goal-driven tasks - a grid-world domain and a robotic object search task. Our experimental results show that our approach exhibits superior generalization performance on both unseen environments and new goals.(1)

关键词： Visualization computer vision Navigation Reinforcement learning Search problems Cognition Planning

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End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution

End-to-End Learning for Joint Image Demosaicing, Denoising a...

引用

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

作者： Xing, Wenzhu Egiazarian, Karen Tampere Univ Computat Image Grp Tampere Finland

ISBN: (纸本)9781665445092

Image denoising, demosaicing and super-resolution are key problems of image restoration well studied in the recent decades. Often, in practice, one has to solve these problems simultaneously. A problem of finding a joint solution of the multiple image restoration tasks just begun to attract an increased attention of researchers. In this paper, we propose an end-to-end solution for the joint demosaicing, denoising and super-resolution based on a specially designed deep convolutional neural network (CNN). We systematically study different methods to solve this problem and compared them with the proposed method. Extensive experiments carried out on large image datasets demonstrate that our method outperforms the state-of-the-art both quantitatively and qualitatively. Finally, we have applied various loss functions in the proposed scheme and demonstrate that by using the mean absolute error as a loss function, we can obtain superior results in comparison to other cases.

关键词： Training Superresolution Noise reduction Switches Image restoration pattern recognition Convolutional neural networks

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