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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是1441-1450 订阅

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Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer

Towards Weakly-Supervised Text Spotting using a Multi-Task T...

引用

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

作者： Kittenplon, Yair Lavi, Inbal Fogel, Sharon Bar, Yarin Manmatha, R. Perona, Pietro AWS AI Labs Cambridge England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We introduce TextTranSpotter (EIS), a transformer-based approach for text spotting and the first text spotting framework which may be trained with both fully- and weakly-supervised settings. By learning a single latent representation per word detection, and using a novel loss function based on the Hungarian loss, our method alleviates the need for expensive localization annotations. Trained with only text transcription annotations on real data, our weakly-supervised method achieves competitive performance with previous state-of-the-art fully-supervised methods. When trained in a fully-supervised manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks.

关键词： Training Location awareness computer vision Annotations Text recognition Benchmark testing Transformers

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Programmatic Concept Learning for Human Motion Description and Synthesis

Programmatic Concept Learning for Human Motion Description a...

引用

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

作者： Kulal, Sumith Mao, Jiayuan Aiken, Alex Wu, Jiajun Stanford Univ Stanford CA 94305 USA MIT Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low-level motion and high-level description as motion concepts. This representation enables human motion description, interactive editing, and controlled synthesis of novel video sequences within a single framework. We present an architecture that learns this concept representation from paired video and action sequences in a semi-supervised manner. The compactness of our representation also allows us to present a low-resource training recipe for data-efficient learning. By outperforming established baselines, especially in the small data regime, we demonstrate the efficiency and effectiveness of our framework for multiple applications.

关键词： Training computer vision Video sequences computer architecture pattern recognition

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A Low-cost & Real-time Motion Capture System

A Low-cost & Real-time Motion Capture System

引用

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

作者： Chatzitofis, Anargyros Albanis, Georgios Zioulis, Nikolaos Thermos, Spyridon Codewheel Larnaka Larnaca Cyprus Univ Thessaly Dept Informat & Telecommun Volos Greece

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption. In this work, we demonstrate such a system but rely on a very sparse set of low-cost consumer-grade sensors. Our system exploits a data-driven backend to infer the captured subject's joint positions from noisy marker estimates in real-time. In addition to reduced costs and portability, its inherent denoising nature allows for quicker captures by alleviating the need for precise marker placement and post-processing, making it suitable for interactive virtual reality applications.

关键词： computer vision Costs Noise reduction Virtual reality Motion capture Sensor systems Real-time systems

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Accelerating Video Object Segmentation with Compressed Video

Accelerating Video Object Segmentation with Compressed Video

引用

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

作者： Xu, Kai Yao, Angela Natl Univ Singapore Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream. Specifically, we propose a motion vector-based warping method for propagating segmentation masks from keyframes to other frames in a bidirectional and multi-hop manner. Additionally, we introduce a residual-based correction module that can fix wrongly propagated segmentation masks from noisy or erroneous motion vectors. Our approach is flexible and can be added on top of several existing video object segmentation algorithms. We achieved highly competitive results on DAVIS17 and YouTube-VOS on various base models with substantial speed-ups of up to 3.5X with minor drops in accuracy.(1)

关键词： computer vision Analytical models Tracking Shape Motion segmentation Computational modeling Redundancy

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Total Variation Optimization Layers for computer vision

Total Variation Optimization Layers for Computer Vision

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

作者： Yeh, Raymond A. Hu, Yuan-Ting Ren, Zhongzheng Schwing, Alexander G. Toyota Technol Inst Chicago IL 60637 USA Univ Illinois Champaign IL USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization problem within a layer is useful?;(b) how to ensure that computation within a layer remains efficient? To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets too. We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising, improving over existing baselines. To achieve these results we had to address question (b): we developed a GPU-based projectedNewton method which is 37x faster than existing solutions.

关键词： Location awareness computer vision TV Smoothing methods Image edge detection Minimization pattern recognition

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Deformable Sprites for Unsupervised Video Decomposition

Deformable Sprites for Unsupervised Video Decomposition

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

作者： Ye, Vickie Li, Zhengqi Tucker, Richard Kanazawa, Angjoo Snavely, Noah Univ Calif Berkeley Berkeley CA 94720 USA Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We describe a method to extract persistent elements of a dynamic scene from an input video. We represent each scene element as a Deformable Sprite consisting of three components: 1) a 2D texture image for the entire video, 2) per-frame masks for the element, and 3) non-rigid deformations that map the texture image into each video frame. The resulting decomposition allows for applications such as consistent video editing. Deformable Sprites are a type of video auto-encoder model that is optimized on individual videos, and does not require training on a large dataset, nor does it rely on pre-trained models. Moreover, our method does not require object masks or other user input, and discovers moving objects of a wider variety than previous work. We evaluate our approach on standard video datasets and show qualitative results on a diverse array of Internet videos.

关键词： Deformable models Training computer vision pattern recognition Internet Sprites (computer) Standards

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Grounding Answers for Visual Questions Asked by Visually Impaired People

Grounding Answers for Visual Questions Asked by Visually Imp...

引用

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

作者： Chen, Chongyan Anjum, Samreen Gurari, Danna Univ Texas Austin Austin TX 78712 USA Univ Colorado Boulder CO 80309 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Visual question answering is the task of answering questions about images. We introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually grounds answers to visual questions asked by people with visual impairments. We analyze our dataset and compare it with five VQA-Grounding datasets to demonstrate what makes it similar and different. We then evaluate the SOTA VQA and VQA-Grounding models and demonstrate that current SOTA algorithms often fail to identify the correct visual evidence where the answer is located. These models regularly struggle when the visual evidence occupies a small fraction of the image, for images that are higher quality, as well as for visual questions that require skills in text recognition. The dataset, evaluation server, and leader-board all can be found at the following link:https://***/tasks-and-datasets/answergrounding-for-vqa/.

关键词： Visualization computer vision Correlation Grounding Text recognition Computational modeling Visual impairment

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Leveling Down in computer vision: Pareto Inefficiencies in Fair Deep Classifiers

Leveling Down in Computer Vision: Pareto Inefficiencies in F...

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

作者： Zietlow, Dominik Lohaus, Michael Balakrishnan, Guha Kleindessner, Matthaeus Locatello, Francesco Schoelkopf, Bernhard Russell, Chris Amazon Web Serv Tubingen Germany MPI IS Tubingen Tubingen Germany Univ Tubingen Tubingen Germany Rice Univ Houston TX 77251 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups.

关键词： Degradation computer vision Ethics Adaptation models Computational modeling Buildings Network architecture

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Self-supervised Deep Image Restoration via Adaptive Stochastic Gradient Langevin Dynamics

Self-supervised Deep Image Restoration via Adaptive Stochast...

引用

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

作者： Wang, Weixi Li, Ji Ji, Hui Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While supervised deep learning has been a prominent tool for solving many image restoration problems, there is an increasing interest on studying self-supervised or unsupervised methods to address the challenges and costs of collecting truth images. Based on the neuralization of a Bayesian estimator of the problem, this paper presents a self-supervised deep learning approach to general image restoration problems. The key ingredient of the neuralized estimator is an adaptive stochastic gradient Langevin dynamics algorithm for efficiently sampling the posterior distribution of network weights. The proposed method is applied on two image restoration problems: compressed sensing and phase retrieval. The experiments on these applications showed that the proposed method not only outperformed existing non-learning and unsupervised solutions in terms of image restoration quality, but also is more computationally efficient.

关键词： Deep learning computer vision Costs Image restoration Computational efficiency pattern recognition Bayes methods

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SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings

SurfEmb: Dense and Continuous Correspondence Distributions f...

引用

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

作者： Haugaard, Rasmus Laurvig Buch, Anders Glent Univ Southern Denmark Odense Denmark

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present an approach to learn dense, continuous 2D-3D correspondence distributions over the surface of objects from data with no prior knowledge of visual ambiguities like symmetry. We also present a new method for 6D pose estimation of rigid objects using the learnt distributions to sample, score and refine pose hypotheses. The correspondence distributions are learnt with a contrastive loss, represented in object-specific latent spaces by an encoder-decoder query model and a small fully connected key model. Our method is unsupervised with respect to visual ambiguities, yet we show that the query- and key models learn to represent accurate multi-modal surface distributions. Our pose estimation method improves the state-of-the-art significantly on the comprehensive BOP Challenge, trained purely on synthetic data, even compared with methods trained on real data. The project site is at ***.

关键词： Visualization computer vision Computational modeling Pose estimation Color pattern recognition

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