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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22905 条记录，以下是4941-4950 订阅

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Temporally Coherent Interpretations for Long Videos Using pattern Theory

Temporally Coherent Interpretations for Long Videos Using Pa...

引用

ieee conference on computer vision and pattern recognition (CVPR)

作者： Souza, Fillipe Sarkar, Sudeep Srivastava, Anuj Su, Jingyong Univ S Florida Tampa FL 33620 USA Florida State Univ Tallahassee FL 32306 USA Texas Tech Univ Lubbock TX 79409 USA

ISBN: (纸本)9781467369640

Graph-theoretical methods have successfully provided semantic and structural interpretations of images and videos. A recent paper introduced a pattern-theoretic approach that allows construction of flexible graphs for representing interactions of actors with objects and inference is accomplished by an efficient annealing algorithm. Actions and objects are termed generators and their interactions are termed bonds;together they form high-probability configurations, or interpretations, of observed scenes. This work and other structural methods have generally been limited to analyzing short videos involving isolated actions. Here we provide an extension that uses additional temporal bonds across individual actions to enable semantic interpretations of longer videos. Longer temporal connections improve scene interpretations as they help discard (temporally) local solutions in favor of globally superior ones. Using this extension, we demonstrate improvements in understanding longer videos, compared to individual interpretations of non-overlapping time segments. We verified the success of our approach by generating interpretations for more than 700 video segments from the YouCook data set, with intricate videos that exhibit cluttered background, scenarios of occlusion, viewpoint variations and changing conditions of illumination. Interpretations for long video segments were able to yield performance increases of about 70% and, in addition, proved to be more robust to different severe scenarios of classification errors.

关键词： Graph theory

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Image Animation with Perturbed Masks

Image Animation with Perturbed Masks

引用

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

作者： Shalev, Yoav Wolf, Lior Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object. We do not assume the existence of pose models and our method is able to animate arbitrary objects without the knowledge of the object's structure. Furthermore, both, the driving video and the source image are only seen during test-time. Our method is based on a shared mask generator, which separates the foreground object from its background, and captures the object's general pose and shape. To control the source of the identity of the output frame, we employ perturbations to interrupt the unwanted identity information on the driver's mask. A mask-refinement module then replaces the identity of the driver with the identity of the source. Conditioned on the source image, the transformed mask is then decoded by a multiscale generator that renders a realistic image, in which the content of the source frame is animated by the pose in the driving video. Due to the lack of fully supervised data, we train on the task of reconstructing frames from the same video the source image is taken from. Our method is shown to greatly outperform the state-of-the-art methods on multiple benchmarks. Our code and samples are available at https://***/itsyoayshalev/Image-Animation-with-Perturbed-Masks.

关键词： computer vision Codes Shape Perturbation methods Benchmark testing Generators Encoding

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Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning

Unsupervised Representation Learning for Binary Networks by ...

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

作者： Kim, Dahyun Choi, Jonghyun Upstage AI Res Yongin South Korea NAVER AI Lab Seongnam South Korea Yonsei Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Self-supervised learning is a promising unsupervised learning framework that has achieved success with large floating point networks. But such networks are not readily deployable to edge devices. To accelerate deployment of models with the benefit of unsupervised representation learning to such resource limited devices for various downstream tasks, we propose a self-supervised learning method for binary networks that uses a moving target network. In particular, we propose to jointly train a randomly initialized classifier, attached to a pretrained floating point feature extractor, with a binary network. Additionally, we propose a feature similarity loss, a dynamic loss balancing and modified multi-stage training to further improve the accuracy, and call our method BURN. Our empirical validations over five downstream tasks using seven datasets show that BURN outperforms self-supervised baselines for binary networks and sometimes outperforms supervised pretraining. Code is availabe at https://***/naver-ai/burn.

关键词： Representation learning Training Codes Surveillance Self-supervised learning vision sensors Feature extraction

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Accelerating DETR Convergence via Semantic-Aligned Matching

Accelerating DETR Convergence via Semantic-Aligned Matching

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

作者： Zhang, Gongjie Luo, Zhipeng Yu, Yingchen Cui, Kaiwen Lu, Shijian Nanyang Technol Univ Singapore Singapore SenseTime Res Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The recently developed DEtection TRansformer (DETR) establishes a new object detection paradigm by eliminating a series of hand-crafted components. However, DETR suffers from extremely slow convergence, which increases the training cost significantly. We observe that the slow convergence is largely attributed to the complication in matching object queries with target features in different feature embedding spaces. This paper presents SAM-DETR, a Semantic-Aligned-Matching DETR that greatly accelerates DETR's convergence without sacrificing its accuracy. SAM-DETR addresses the convergence issue from two perspectives. First, it projects object queries into the same embedding space as encoded image features, where the matching can be accomplished efficiently with aligned semantics. Second, it explicitly searches salient points with the most discriminative features for semantic-aligned matching, which further speeds up the convergence and boosts detection accuracy as well. Being like a plug and play, SAM-DETR complements existing convergence solutions well yet only introduces slight computational overhead. Extensive experiments show that the proposed SAM-DETR achieves superior convergence as well as competitive detection accuracy. The implementation codes are publicly available at https://***/ZhangGongjie/SAM-DETR.

关键词： Training computer vision Costs Semantics Object detection Transformers Feature extraction

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Angelic Patches for Improving Third-Party Object Detector Performance

Angelic Patches for Improving Third-Party Object Detector Pe...

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

作者： Si, Wenwen Li, Shuo Park, Sangdon Lee, Insup Bastani, Osbert Univ Penn Dept Comp & Info Sci Philadelphia PA 19104 USA Georgia Inst Technol Sch Cybersecur & Privacy Atlanta GA 30332 USA

ISBN: (纸本)9798350301298

Deep learning models have shown extreme vulnerability to distribution shifts such as synthetic perturbations and spatial transformations. In this work, we explore whether we can adopt the characteristics of adversarial attack methods to help improve robustness of object detection to distribution shifts such as synthetic perturbations and spatial transformations. We study a class of realistic object detection settings wherein the target objects have control over their appearance. To this end, we propose a reversed Fast Gradient Sign Method (FGSM) to obtain these angelic patches that significantly increase the detection probability, even without pre-knowledge of the perturbations. In detail, we apply the patch to each object instance simultaneously, strengthening not only classification, but also bounding box accuracy. Experiments demonstrate the efficacy of the partial-covering patch in solving the complex bounding box problem. More importantly, the performance is also transferable to different detection models even under severe affine transformations and deformable shapes. To the best of our knowledge, we are the first object detection patch that achieves both cross-model efficacy and multiple patches. We observed average accuracy improvements of 30% in the real-world experiments. Our code is available at: https: //***/averysi224/angelic_patches.

关键词： computer vision for social good

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Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization

Ray3D: ray-based 3D human pose estimation for monocular abso...

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

作者： Zhan, Yu Li, Fenghai Weng, Renliang Choi, Wongun Aibee Inc Beijing Peoples R China Beijing Technol & Business Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models.

关键词： Location awareness Solid modeling computer vision Three-dimensional displays Systematics Image analysis Pose estimation

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Scan2Mesh: From Unstructured Range Scans to 3D Meshes 32

Scan2Mesh: From Unstructured Range Scans to 3D Meshes

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dai, Angela Niessner, Matthias Tech Univ Munich Munich Germany

ISBN: (纸本)9781728132938

We introduce Scan2Mesh, a novel data-driven generative approach which transforms an unstructured and potentially incomplete range scan into a structured 3D mesh representation. The main contribution of this work is a generative neural network architecture whose input is a range scan of a 3D object and whose output is an indexed face set conditioned on the input scan. In order to generate a 3D mesh as a set of vertices and face indices, the generative model builds on a series of proxy losses for vertices, edges, and faces. At each stage, we realize a one-to-one discrete mapping between the predicted and ground truth data points with a combination of convolutional- and graph neural network architectures. This enables our algorithm to predict a compact mesh representation similar to those created through manual artist effort using 3D modeling software. Our generated mesh results thus produce sharper, cleaner meshes with a fundamentally different structure from those generated through implicit functions, a first step in bridging the gap towards artist-created CAD models.

关键词： 3D from Multiview and Sensors vision + Graphics

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Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo

Multi-View Multi-Person 3D Pose Estimation with Plane Sweep ...

引用

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

作者： Lin, Jiahao Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781665445092

Existing approaches for multi-view multi-person 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views and solve for the 3D pose estimation for each person. Establishing cross-view correspondences is challenging in multi-person scenes, and incorrect correspondences will lead to sub-optimal performance for the multi-stage pipeline. In this work, we present our multi-view 3D pose estimation approach based on plane sweep stereo to jointly address the cross-view fusion and 3D pose reconstruction in a single shot. Specifically, we propose to perform depth regression for each joint of each 2D pose in a target camera view. Cross-view consistency constraints are implicitly enforced by multiple reference camera views via the plane sweep algorithm to facilitate accurate depth regression. We adopt a coarse-to-fine scheme to first regress the person-level depth followed by a per-person joint-level relative depth estimation. 3D poses are obtained from a simple back-projection given the estimated depths. We evaluate our approach on benchmark datasets where it outperforms previous state-of-the-arts while being remarkably efficient. Our code is available at the project website.(1)

关键词： computer vision Three-dimensional displays Codes Pose estimation Pipelines Benchmark testing Cameras

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TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing

TransEditor: Transformer-Based Dual-Space GAN for Highly Con...

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

作者： Xu, Yanbo Yin, Yueqin Jiang, Liming Wu, Qianyi Zheng, Chengyao Loy, Chen Change Dai, Bo Wu, Wayne Shanghai AI Lab Shanghai Peoples R China Nanyang Technol Univ S Lab Singapore Singapore SenseTime Res Hong Kong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Monash Univ Melbourne Vic Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for better disentanglement of style and content representations. Nonetheless, these methods are still incompetent to obtain plausible editing results with high controllability, especially for complicated attributes. In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing. We propose TransEditor, a novel Transformer-based framework to enhance such interaction. Besides, we develop a new dual-space editing and inversion strategy to provide additional editing flexibility. Extensive experiments demonstrate the superiority of the proposed framework in image quality and editing capability, suggesting the effectiveness of TransEditor for highly controllable facial editing. Code and models are publicly available at https://***/BillyXYB/TransEditor.

关键词： Image quality computer vision Codes Computational modeling Aerospace electronics Transformers Controllability

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Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching

Cascade Cost Volume for High-Resolution Multi-View Stereo an...

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

作者： Gu, Xiaodong Fan, Zhiwen Zhu, Siyu Dai, Zuozhuo Tan, Feitong Tan, Ping Alibaba AI Labs Hangzhou Peoples R China Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781728171685

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the depth or disparity. These methods are limited with high-resolution outputs since the memory and time costs grow cubically as the volume resolution increases. In this paper, we propose a memory and time efficient cost volume formulation complementary to existing multi-view stereo and stereo matching approaches based on 3D cost volumes. First, the proposed cost volume is built upon a feature pyramid encoding geometry and context at gradually finer scales. Then, we can narrow the depth (or disparity) range of each stage by the prediction from the previous stage. With gradually higher cost volume resolution and adaptive adjustment of depth (or disparity) intervals, the output is recovered in a coarser to fine manner. We apply the cascade cost volume to the representative MVS-Net, and obtain a 35.6% improvement on DTU bench-mark (1st place), with 50.6% and 59.3% reduction in GPU memory and run-time. It is also rank first on Tanks and Temples benchmark of all deep models. The statistics of accuracy, run-time and GPU memory on other representative stereo CNNs also validate the effectiveness of our proposed method. Our source code is available at https : //github. com/alibaba/cascade-stereo.

关键词： computer vision

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