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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61-70 订阅

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A Decomposition Model for Stereo Matching

A Decomposition Model for Stereo Matching

引用

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

作者： Yao, Chengtang Jia, Yunde Di, Huijun Li, Pengxiang Wu, Yuwei Beijing Inst Technol Beijing Lab Intelligent Informat Technol Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases. In order to reduce the huge cost of stereo matching at the original resolution, our model only runs dense matching at a very low resolution and uses sparse matching at different higher resolutions to recover the disparity of lost details scale-by-scale. After the decomposition of stereo matching, our model iteratively fuses the sparse and dense disparity maps from adjacent scales with an occlusion-aware mask. A refinement network is also applied to improving the fusion result. Compared with high-performance methods like PSMNet and GANet, our method achieves 10 - 100 x speed increase while obtaining comparable disparity estimation results.

关键词： computer vision Costs Fuses Computational modeling Face recognition Pipelines Estimation

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No Shadow Left Behind: Removing Objects and their Shadows using Approximate Lighting and Geometry

No Shadow Left Behind: Removing Objects and their Shadows us...

引用

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

作者： Zhang, Edward Martin-Brualla, Ricardo Kontkanen, Janne Curless, Brian Univ Washington Seattle WA 98195 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Removing objects from images is a challenging technical problem that is important for many applications, including mixed reality. For believable results, the shadows that the object casts should also be removed. Current inpainting-based methods only remove the object itself, leaving shadows behind, or at best require specifying shadow regions to inpaint. We introduce a deep learning pipeline for removing a shadow along with its caster. We leverage rough scene models in order to remove a wide variety of shadows (hard or soft, dark or subtle, large or thin) from surfaces with a wide variety of textures. We train our pipeline on synthetically rendered data, and show qualitative and quantitative results on both synthetic and real scenes.

关键词： Geometry Deep learning computer vision Pipelines Mixed reality Lighting Surface roughness

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Exploring Simple Siamese Representation Learning

Exploring Simple Siamese Representation Learning

引用

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

作者： Chen, Xinlei He, Kaiming Facebook AI Res FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it. Our "SimSiam" method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning. Code is made available.(1)

关键词： Visualization computer vision Codes Shape Computational modeling computer architecture Tools

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TPCN: Temporal Point Cloud Networks for Motion Forecasting

TPCN: Temporal Point Cloud Networks for Motion Forecasting

引用

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

作者： Ye, Maosheng Cao, Tongyi Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China DEEPROUTE AI Fremont CA USA

ISBN: (纸本)9781665445092

We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible framework with joint spatial and temporal learning for trajectory prediction. Unlike existing approaches that rasterize agents and map information as 2D images or operate in a graph representation, our approach extends ideas from point cloud learning with dynamic temporal learning to capture both spatial and temporal information by splitting trajectory prediction into both spatial and temporal dimensions. In the spatial dimension, agents can be viewed as an unordered point set, and thus it is straightforward to apply point cloud learning techniques to model agents' locations. While the spatial dimension does not take kinematic and motion information into account, we further propose dynamic temporal learning to model agents' motion over time. Experiments on the Argoverse motion forecasting benchmark show that our approach achieves state-of-the-art results.

关键词： computer vision Dynamics Kinematics computer architecture Benchmark testing Predictive models Trajectory

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Seeing in Extra Darkness Using a Deep-Red Flash

Seeing in Extra Darkness Using a Deep-Red Flash

引用

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

作者： Xiong, Jinhui Wang, Jian Heidrich, Wolfgang Nayar, Shree KAUST Daejeon South Korea Snap Res Daejeon South Korea

ISBN: (纸本)9781665445092

We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source. Our main observation is that in a dim environment, the human eye mainly uses rods for the perception of light, which are not sensitive to wavelengths longer than 620 nm, yet the camera sensor still has a spectral response. We propose a novel modulation strategy when training a modern CNN model for guided image filtering, fusing a noisy RGB frame and a flash frame. This fusion network is further extended for video reconstruction. We have built a prototype with minor hardware adjustments and tested the new flash technique on a variety of static and dynamic scenes. The experimental results demonstrate that our method produces compelling reconstructions, even in extra dim conditions.

关键词： Training Photography Sensitivity Modulation Prototypes Cameras pattern recognition

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Mining Better Samples for Contrastive Learning of Temporal Correspondence

Mining Better Samples for Contrastive Learning of Temporal C...

引用

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

作者： Jeon, Sangryul Min, Dongbo Kim, Seungryong Sohn, Kwanghoon Yonsei Univ Seoul South Korea Ewha Womans Univ Seoul South Korea Korea Univ Seoul South Korea

ISBN: (纸本)9781665445092

We present a novel framework for contrastive learning of pixel-level representation using only unlabeled video. Without the need of ground-truth annotation, our method is capable of collecting well-defined positive correspondences by measuring their confidences and well-defined negative ones by appropriately adjusting their hardness during training. This allows us to suppress the adverse impact of ambiguous matches and prevent a trivial solution from being yielded by too hard or too easy negative samples. To accomplish this, we incorporate three different criteria that ranges from a pixel-level matching confidence to a video-level one into a bottom-up pipeline, and plan a curriculum that is aware of current representation power for the adaptive hardness of negative samples during training. With the proposed method, state-of-the-art performance is attained over the latest approaches on several video label propagation tasks.

关键词： Training computer vision Uncertainty Annotations Pipelines Measurement uncertainty pattern recognition

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MagFace: A Universal Representation for Face recognition and Quality Assessment

MagFace: A Universal Representation for Face Recognition and...

引用

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

作者： Meng, Qiang Zhao, Shichao Huang, Zhida Zhou, Feng Aibee Inc Algorithm Res Beijing Peoples R China

ISBN: (纸本)9781665445092

The performance of face recognition system degrades when the variability of the acquired faces increases. Prior work alleviates this issue by either monitoring the face quality in pre-processing or predicting the data uncertainty along with the face feature. This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face. Under the new loss, it can be proven that the magnitude of the feature embedding monotonically increases if the subject is more likely to be recognized. In addition, MagFace introduces an adaptive mechanism to learn a wellstructured within-class feature distributions by pulling easy samples to class centers while pushing hard samples away. This prevents models from overfitting on noisy low-quality samples and improves face recognition in the wild. Extensive experiments conducted on face recognition, quality assessments as well as clustering demonstrate its superiority over state-of-the-arts.

关键词： computer vision Uncertainty Face recognition Loss measurement Quality assessment Object recognition Noise measurement

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Crossing cuts polygonal puzzles: Models and Solvers

Crossing cuts polygonal puzzles: Models and Solvers

引用

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

作者： Harel, Peleg Ben-Shahar, Ohad

ISBN: (纸本)9781665445092

Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered fragments, is fundamental to numerous applications, and yet most of the literature has focused thus far on less realistic puzzles whose pieces are identical squares. Here we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape with an arbitrary number of straight cuts. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical system endowed with hierarchical loop constraints and a layered reconstruction process that is guided by the pictorial content of the pieces. We define evaluation metrics and present experimental results on both apictorial and pictorial puzzles to indicate that they are solvable completely automatically.

关键词： Measurement computer vision Shape Computational modeling pattern recognition

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Image Super-Resolution with Non-Local Sparse Attention

Image Super-Resolution with Non-Local Sparse Attention

引用

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

作者： Mei, Yiqun Fan, Yuchen Zhou, Yuqian Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665445092

Both Non-Local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern. NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation. Specifically, NLSA rectifies non-local attention with spherical locality sensitive hashing (LSH) that partitions the input space into hash buckets of related features. For every query signal, NLSA assigns a bucket to it and only computes attention within the bucket. The resulting sparse attention prevents the model from attending to locations that are noisy and less-informative, while reducing the computational cost from quadratic to asymptotic linear with respect to the spatial size. Extensive experiments validate the effectiveness and efficiency of NLSA. With a few non-local sparse attention modules, our architecture, called non-local sparse network (NLSN), reaches state-of-the-art performance for SISR quantitatively and qualitatively.

关键词： computer vision Computational modeling Superresolution computer architecture Benchmark testing Robustness pattern recognition

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Indoor Lighting Estimation using an Event Camera

Indoor Lighting Estimation using an Event Camera

引用

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

作者： Chen, Zehao Zheng, Qian Niu, Peisong Tang, Huajin Pan, Gang Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781665445092

Image-based methods for indoor lighting estimation suffer from the problem of intensity-distance ambiguity. This paper introduces a novel setup to help alleviate the ambiguity based on the event camera. We further demonstrate that estimating the distance of a light source becomes a well-posed problem under this setup, based on which an optimization-based method and a learning-based method are proposed. Our experimental results validate that our approaches not only achieve superior performance for indoor lighting estimation (especially for the close light) but also significantly alleviate the intensity-distance ambiguity.

关键词： Learning systems computer vision Lighting Estimation Dynamic range Cameras Turning

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