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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141-150 订阅

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Pose recognition with Cascade Transformers

Pose Recognition with Cascade Transformers

引用

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

作者： Li, Ke Wang, Shijie Zhang, Xiang Xu, Yifan Xu, Weijian Tu, Zhuowen Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781665445092

In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.

关键词： Heating systems Visualization computer vision Transformers pattern recognition Decoding Task analysis

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SwiftNet: Real-time Video Object Segmentation

SwiftNet: Real-time Video Object Segmentation

引用

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

作者： Wang, Haochen Jiang, Xiaolong Ren, Haibing Hu, Yao Bai, Song Alibaba Youku Cognit & Intelligent Lab Beijing Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77.8% J&F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance. We achieve this by elaborately compressing spatiotemporal redundancy in matching-based VOS via Pixel-Adaptive Memory (PAM). Temporally, PAM adaptively triggers memory updates on frames where objects display noteworthy inter-frame variations. Spatially, PAM selectively performs memory update and match on dynamic pixels while ignoring the static ones, significantly reducing redundant computations wasted on segmentation-irrelevant pixels. To promote efficient reference encoding, light-aggregation encoder is also introduced in SwiftNet deploying reversed sub-pixel. We hope SwiftNet could set a strong and efficient baseline for real-time VOS and facilitate its application in mobile vision.

关键词： computer vision Codes Redundancy Memory management Object segmentation Streaming media Real-time systems

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Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times

Digital Gimbal: End-to-end Deep Image Stabilization with Lea...

引用

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

作者： Dahary, Omer Jacoby, Matan Bronstein, Alex M. Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9781665445092

Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized system from the input of a fast unstabilized camera. To exploit the trade-off between motion blur at long exposures and low SNR at short exposures, we train a CNN that estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure frames, related by unknown motion. We further suggest learning the burst's exposure times in an end-to-end manner, thus balancing the noise and blur across the frames. We demonstrate this method's advantage over the traditional approach of deblurring a single image or denoising a fixed-exposure burst on both synthetic and real data.

关键词： computer vision Limiting Noise reduction Cameras pattern recognition Noise measurement Image reconstruction

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Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

Hybrid Rotation Averaging: A Fast and Robust Rotation Averag...

引用

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

作者： Chen, Yu Zhao, Ji Kneip, Laurent Peking Univ Beijing Peoples R China TuSimple San Diego CA USA ShanghaiTech Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

We address rotation averaging (RA) and its application to real-world 3D reconstruction. Local optimisation based approaches are the de facto choice, though they only guarantee a local optimum. Global optimisers ensure global optimality in low noise conditions, but they are inefficient and may easily deviate under the influence of outliers or elevated noise levels. We push the envelope of rotation averaging by leveraging the advantages of a global RA method and a local RA method. Combined with a fast view graph filtering as preprocessing, the proposed hybrid approach is robust to outliers. We further apply the proposed hybrid rotation averaging approach to incremental Structure from Motion (SfM), the accuracy and robustness of SfM are both improved by adding the resulting global rotations as regularisers to bundle adjustment. Overall, we demonstrate high practicality of the proposed method as bad camera poses are effectively corrected and drift is reduced.

关键词： Bundle adjustment computer vision Structure from motion Filtering Pipelines Optimization methods Cameras

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TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations

TearingNet: Point Cloud Autoencoder to Learn Topology-Friend...

引用

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

作者： Pang, Jiahao Li, Duanshun Tian, Dong InterDigital Princeton NJ 08540 USA

ISBN: (纸本)9781665445092

Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tackles the challenging task of representing the point clouds using a fixed-length descriptor. Unlike existing works directly deforming predefined primitives of genus zero (e.g., a 2D square patch) to an object-level point cloud, our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Particularly, the Tearing network module learns the point cloud topology explicitly. By breaking the edges of a primitive graph, it tears the graph into patches or with holes to emulate the topology of a target point cloud, leading to faithful reconstructions. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology friendly representations than benchmarks.

关键词： Deep learning Surface reconstruction Network topology Shape Topology pattern recognition Proposals

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Line Segment Detection Using Transformers without Edges

Line Segment Detection Using Transformers without Edges

引用

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

作者： Xu, Yifan Xu, Weijian Cheung, David Tu, Zhuowen Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781665445092

In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LEM), takes advantages of having integrated tokenized queries, a self-attention mechanism, and encoding-decoding strategy within Transformers by skipping standard heuristic designs for the edge element detection and perceptual grouping processes. We equip Transformers with a multi-scale encoder/decoder strategy to perform fine-grained line segment detection under a direct endpoint distance loss. This loss term is particularly suitable for detecting geometric structures such as line segments that are not conveniently represented by the standard bounding box representations. The Transformers learn to gradually refine line segments through layers of self-attention. In our experiments, we show state-of-the-art results on Wireframe and YorkUrban benchmarks.

关键词： computer vision Casting Image edge detection Computational modeling Detectors Predictive models Transformers

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Weakly Supervised Action Selection Learning in Video

Weakly Supervised Action Selection Learning in Video

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

作者： Ma, Junwei Gorti, Satya Krishna Volkovs, Maksims Yu, Guangwei Layer6 AI Toronto ON Canada

ISBN: (纸本)9781665445092

Localizing actions in video is a core task in computer vision. The weakly supervised temporal localization problem investigates whether this task can be adequately solved with only video-level labels, significantly reducing the amount of expensive and error-prone annotation that is required. A common approach is to train a frame-level classifier where frames with the highest class probability are selected to make a video-level prediction. Frame-level activations are then used for localization. However, the absence of frame-level annotations cause the classifier to impart class bias on every frame. To address this, we propose the Action Selection Learning (ASL) approach to capture the general concept of action, a property we refer to as "actionness". Under ASL, the model is trained with a novel class-agnostic task to predict which frames will be selected by the classifier. Empirically, we show that ASL outperforms leading baselines on two popular benchmarks THUMOS-14 and ActivityNet-I.2, with 10.3% and 5.7% relative improvement respectively. We further anab=e the properties of ASL and demonstrate the importance of actionness. Full code for this work is available here: https://***/layer6ai labs/ASL.

关键词： Location awareness computer vision Codes Annotations Predictive models Benchmark testing pattern recognition

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MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes

MagDR: Mask-guided Detection and Reconstruction for Defendin...

引用

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

作者： Chen, Zhikai Xie, Lingxi Pang, Shanmin He, Yong Zhang, Bo Xi An Jiao Tong Univ Xian Peoples R China Huawei Inc Shenzhen Peoples R China Tencent Blade Team Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR starts with a detection module that defines a few criteria to judge the abnormality of the output of deepfakes, and then uses it to guide a learnable reconstruction procedure. Adaptive masks are extracted to capture the change in local facial regions. In experiments, MagDR defends three main tasks of deepfakes, and the learned reconstruction pipeline transfers across input data, showing promising performance in defending both black-box and white-box attacks.

关键词： Visualization Perturbation methods Pipelines Semantics Prediction algorithms pattern recognition Task analysis

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Reducing Domain Gap by Reducing Style Bias

Reducing Domain Gap by Reducing Style Bias

引用

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

作者： Nam, Hyeonseob Lee, HyunJae Park, Jongchan Yoon, Wonjun Yoo, Donggeun Lunit Inc Seoul South Korea

ISBN: (纸本)9781665445092

Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets.(1)

关键词： computer vision Adaptation models Shape Decision making Aerospace electronics Robustness Encoding

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Playable Video Generation

Playable Video Generation

引用

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

作者： Menapace, Willi Lathuiliere, Stephane Tulyakov, Sergey Siarohin, Aliaksandr Ricci, Elisa Univ Trento Trento Italy Telecom Paris LTC Paris France Inst Polytech Paris Paris France Snap Inc Santa Monica CA USA Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9781665445092

This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety.

关键词： computer vision Codes computer architecture Games pattern recognition Task analysis Unsupervised learning

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