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检索条件"任意字段=2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013"

共 4491 条记录，以下是101-110 订阅

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As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States recognition

As Seen on TV: Automatic Basketball Video Production using G...

引用

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

作者： Quiroga, Julian Carrillo, Henry Maldonado, Edisson Ruiz, John Zapata, Luis M. Genius Sports Comp Vis Medellin Colombia

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Automatic video production of sports aims at producing an aesthetic broadcast of sporting events. We present a new video system able to automatically produce a smooth and pleasant broadcast of Basketball games using a single fixed 4K camera. The system automatically detects and localizes players, ball and referees, to recognize main action coordinates and game states yielding to a professional cameraman-like production of the basketball event. We also release a fully annotated dataset consisting of single 4K camera and twelve-camera videos of basketball games.

关键词： Cameras Games Production Streaming media Robot vision systems Computational modeling

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Diagnosing Rarity in Human-object Interaction Detection

Diagnosing Rarity in Human-object Interaction Detection

引用

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

作者： Kilickaya, Mert Smeulders, Arnold QUvA Deep Vis Lab Amsterdam Netherlands

ISBN: (纸本)9781728193601

Human-object interaction (HOI) detection is a core task in computer vision. The goal is to localize all human-object pairs and recognize their interactions. An interaction defined by a tuple leads to a long-tailed visual recognition challenge since many combinations are rarely represented. The performance of the proposed models is limited especially for the tail categories, but little has been done to understand the reason. To that end, in this paper, we propose to diagnose rarity in HOI detection. We propose a three-step strategy, namely Detection, Identification and recognition where we carefully analyse the limiting factors by studying state-of-the-art models. Our findings indicate that detection and identification steps are altered by the interaction signals like occlusion and relative location, as a result limiting the recognition accuracy.

关键词： computer vision

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Object Tracking by Occlusion Detection via Structured Sparse Learning

Object Tracking by Occlusion Detection via Structured Sparse...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Tianzhu Ghanem, Bernard Xu, Changsheng Ahuja, Narendra Adv Digital Sci Ctr Illinois Singapore Singapore King Abdulaziz Univ Sci Technol Thuwal Saudi Arabia Chinese Acad Sci Inst Automat CSIDM Beijing Peoples R China Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9780769549903

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object's track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art.

关键词： computer vision image representation learning (artificial intelligence) object detection object tracking

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Does Image Anonymization Impact computer vision Training?

Does Image Anonymization Impact Computer Vision Training?

引用

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

作者： Hukkelas, Hakon Lindseth, Frank Norwegian Univ Sci & Technol Dept Comp Sci Trondheim Norway

ISBN: (纸本)9798350302493

Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks.

关键词： computer vision

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Attention! Stay Focus!

Attention! Stay Focus!

引用

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

作者： Vo, Tu BridgeAI Inc Seoul South Korea

ISBN: (纸本)9781665448994

We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set in the NTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge [1] [4].

关键词： Deep learning computer vision conferences computer architecture Feature extraction Cameras Decoding

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Land Cover Classification from Satellite Imagery With U-Net and Lovasz-Softmax Loss 31

Land Cover Classification from Satellite Imagery With U-Net ...

引用

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

作者： Rakhlin, Alexander Davydow, Alex Nikolenko, Sergey Neuromat OU EE-10111 Tallinn Estonia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The land cover classification task of the DeepGlohe Challenge presents significant obstacles even to state of the art segmentation models due to a small amount of data, incomplete and sometimes incorrect labeling, and highly imbalanced classes. In this work, we show an approach based on the U-Net architecture with the Lovcisz-Softmax loss that successfully alleviates these problems: we compare several different convolutional architectures for U-Net encoders.

关键词： Image segmentation Satellites computer architecture Training Task analysis computer vision Stochastic processes

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Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

Revisiting The Evaluation of Class Activation Mapping for Ex...

引用

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

作者： Poppi, Samuele Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781665448994

As the request for deep learning solutions increases, the need for explainability is even more fundamental. In this setting, particular attention has been given to visualization techniques, that try to attribute the right relevance to each input pixel with respect to the output of the network. In this paper, we focus on Class Activation Mapping (CAM) approaches, which provide an effective visualization by taking weighted averages of the activation maps. To enhance the evaluation and the reproducibility of such approaches, we propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches. To evaluate the appropriateness of the proposal, we compare different CAM-based visualization methods on the entire ImageNet validation set, fostering proper comparisons and reproducibility.

关键词： Deep learning Visualization computer vision Protocols conferences Reproducibility of results pattern recognition

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A Stream Algebra for computer vision Pipelines

A Stream Algebra for Computer Vision Pipelines

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27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Helala, Mohamed A. Pu, Ken Q. Qureshi, Faisal Z. Univ Ontario Inst Technol Fac Sci Oshawa ON Canada

ISBN: (纸本)9781479943098

Recent interest in developing online computer vision algorithms is spurred in part by a growth of applications capable of generating large volumes of images and videos. These applications are rich sources of images and video streams. Online vision algorithms for managing, processing and analyzing these streams need to rely upon streaming concepts, such as pipelines, to ensure timely and incremental processing of data. This paper is a first attempt at defining a formal stream algebra that provides a mathematical description of vision pipelines and describes the distributed manipulation of image and video streams. We also show how our algebra can effectively describe the vision pipelines of two state of the art techniques.

关键词： Image and Video Streams Stream Algebra computer vision Pipelines Stream Operators

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Sparklevision: Seeing the World through Random Specular Microfacets

SparkleVision: Seeing the World through Random Specular Micr...

引用

ieee conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Zhengdong Isola, Phillip Adelson, Edward H. MIT Cambridge MA 02139 USA

ISBN: (纸本)9781467367592

In this paper, we study the problem of reproducing the light from a single image of an object covered with random specular microfacets on the surface. We show that such reflectors can be interpreted as a randomized mapping from the lighting to the image. Such specular objects have very different optical properties from both diffuse surfaces and smooth specular objects like metals, so we design a special imaging system to robustly and effectively photograph them. We present simple yet reliable algorithms to calibrate the proposed system and do the inference. We conduct experiments to verify the correctness of our model assumptions and prove the effectiveness of our pipeline.

关键词： Seeing World Random Specular Microfacets

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Decoupling Sparse Coding with Fusion of Fisher Vectors and Scalable SVMs for Large-scale Visual recognition

Decoupling Sparse Coding with Fusion of Fisher Vectors and S...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Ji, Zhengping Samsung Semicond Inc Adv Image Res Lab Pasadena CA 91101 USA

ISBN: (纸本)9780769549903

With the advent of huge collection of images from Internet and emerging mobile devices, large-scale image classification draws amount of research attention in computer vision and AI communities. The advancement of large-scale image classification largely depends on solutions to two problems: how to learn good feature representation from variant scales of pixels, and how to create classification models that can discriminate the feature representation for different semantic meanings of many objects. In this paper, we tackle the first problem by combining different feature representations via sparse coding and Fisher vectors of SIFT and color-based features. To deal with the second problem, we utilize the Averaged Stochastic Gradient Descent (ASGD) algorithm to enable fast and incremental learning of SVMs and further generate confidence values to interpret the likelihood of multiple object categories appearing in the image. We evaluate the proposed learning framework on the ImageNet, a benchmark dataset for large-scale image classification. Our results show favorable performance on a subset of ImageNet containing 196 categories. We also investigate the performance of sparse coding by comparing different combination of algorithms in learning a dictionary and sparse representations. Although there is a natural pair of algorithms to learn a dictionary and sparse representations (e. g., K-SVD with respect to Orthogonal Matching Pursuit), breaking such a pair and rematching are found to result in even better performance. Moreover, detailed comparison indicates that l(1)-regularized solver to sparse representation mainly benefit the classification accuracy, regardless of the choice of dictionaries.

关键词： computer vision feature extraction gradient methods image classification image colour analysis image matching image representation learning (artificial intelligence) stochastic processes support vector machines transforms

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