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

共 22906 条记录，以下是4701-4710 订阅

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Online Joint Multi-Metric Adaptation from Frequent Sharing-Subset Mining for Person Re-Identification

Online Joint Multi-Metric Adaptation from Frequent Sharing-S...

引用

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

作者： Zhou, Jiahuan Su, Bing Wu, Ying Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9781728171685

Person Re-IDentification (P-RID), as an instance-level recognition problem, still remains challenging in computer vision community. Many P-RID works aim to learn faithful and discriminative features/metrics from offline training data and directly use them for the unseen online testing data. However, their performance is largely limited due to the severe data shifting issue between training and testing data. Therefore, we propose an online joint multi-metric adaptation model to adapt the offline learned P-RID models for the online data by learning a series of metrics for all the sharing-subsets. Each sharing-subset is obtained from the proposed novel frequent sharing-subset mining module and contains a group of testing samples which share strong visual similarity relationships to each other. Unlike existing online P-RID methods, our model simultaneously takes both the sample-specific discriminant and the set-based visual similarity among testing samples into consideration so that the adapted multiple metrics can refine the discriminant of all the given testing samples jointly via a multi-kernel late fusion framework. Our proposed model is generally suitable to any offline learned P-RID baselines for online boosting, the performance improvement by our model is not only verified by extensive experiments on several widely-used P-RID benchmarks (CUHK03, Market 1501, DukeMTMC-reID and MSMTI7) and state-of-the-art P-RID baselines but also guaranteed by the provided in-depth theoretical analyses.

关键词： computer vision

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computer vision aided target linked radiation imaging

Computer vision aided target linked radiation imaging

引用

2012 ieee conference on computer vision and pattern recognition, CVPR 2012

作者： Gao, Dashan Yao, Yi Pan, Feng Yu, Ting Yu, Bing Guan, Li Dixon, Walter Yanoff, Brian Tian, Tai-Peng Krahnstoever, Nils GE Global Research Niskayuna NY 12309 United States Google Inc. Cambridge MA 02142 United States

ISBN: (纸本)9781467312264

In this paper, we demonstrated an application of video tracking to radiation detection, where a vision-based tracking system enables a traditional CZT (cadmium zinc telluride)-based radiation imaging device to detect radioactive targets that are in motion. An integrated real-time system consisting of multiple fixed cameras and radiation detectors was implemented and tested. The multi-camera tracking system combines multiple feature cues (such as silhouette, appearance, and geometry) from different viewing angles to ensure consistent target identities under challenging tracking conditions. Experimental results show that both the video tracking and the integrated systems perform accurately and persistently under various scenarios involving multiple vehicles, driving speeds, and driving patterns. The results also validate and reiterate the importance of video tracking as an enabling technology in the field of radiation imaging. © 2012 ieee.

关键词： II-VI semiconductors

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RPN Prototype Alignment For Domain Adaptive Object Detector

RPN Prototype Alignment For Domain Adaptive Object Detector

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

作者： Zhang, Yixin Wang, Zilei Mao, Yushi Univ Sci & Technol China Dept Automat Hefei Peoples R China

ISBN: (纸本)9781665445092

Recent years have witnessed great progress of object detection. However, due to the domain shift problem, applying the knowledge of an object detector learned from one specific domain to another one often suffers severe performance degradation. Most existing methods adopt feature alignment either on the backbone network or instance classifier to increase the transferability of object detector. Differently, we propose to perform feature alignment in the RPN stage such that the foreground and background RPN proposals in target domain can be effectively distinguished. Specifically, we first construct one set of learnable RPN prototpyes, and then enforce the RPN features to align with the prototypes for both source and target domains. It essentially cooperates the learning of RPN prototypes and features to align the source and target RPN features. Particularly, we propose a simple yet effective method suitable for RPN feature alignment to generate high-quality pseudo label of proposals in target domain, i.e., using the filtered detection results with IoU. Furthermore, we adopt Grad CAM to find the discriminative region within a foreground proposal and use it to increase the discriminability of RPN features for alignment. We conduct extensive experiments on multiple cross-domain detection scenarios, and the results show the effectiveness of our proposed method against previous stateof-the-art methods.

关键词： Degradation computer vision Prototypes Detectors Object detection Feature extraction pattern recognition

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Edges to Shapes to Concepts: Adversarial Augmentation for Robust vision

Edges to Shapes to Concepts: Adversarial Augmentation for Ro...

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

作者： Tripathi, Aditay Singh, Rishubh Chakraborty, Anirban Shenoy, Pradeep Indian Inst Sci CDS Bangalore Karnataka India Google Res India Bangalore Karnataka India

ISBN: (纸本)9798350301298

Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViTB) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy.

关键词： Deep learning architectures and techniques

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FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

FedCor: Correlation-Based Active Client Selection Strategy f...

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

作者： Tang, Minxue Ning, Xuefei Wang, Yitu Sun, Jingwei Wang, Yu Li, Hai Chen, Yiran Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the convergence rate of the FL process. Active client selection strategies are popularly proposed in recent studies. However, they neglect the loss correlations between the clients and achieve only marginal improvement compared to the uniform selection strategy. In this work, we propose FedCor-an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). Based on the GP model, we derive a client selection strategy with a significant reduction of expected global loss in each round. Besides, we develop an efficient GP training method with a low communication overhead in the FL scenario by utilizing the covariance stationarity. Our experimental results show that compared to the state-of-the-art method, FedCorr can improve the convergence rates by 34% similar to 99% and 26% similar to 51% on FMNIST and CIFAR-10, respectively.

关键词： Training computer vision Correlation Federated learning Gaussian processes pattern recognition Convergence

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Mixture trees for modeling and fast conditional sampling with applications in vision and graphics

Mixture trees for modeling and fast conditional sampling wit...

引用

2005 ieee computer society conference on computer vision and pattern recognition, CVPR 2005

作者： Dellaert, Frank Kwatra, Vivek Oh, Sang Min College of Computing Georgia Institute of Technology Atlanta GA United States

ISBN: (纸本)0769523722

We introduce mixture trees, a tree-based data-structure for modeling joint probability densities using a greedy hierarchical density estimation scheme. We show that the mixture tree models data efficiently at multiple resolutions, and present fast conditional sampling as one of many possible applications. In particular, the development of this data-structure was spurred by a multi-target tracking application, where memory-based motion modeling calls for fast conditional sampling from large empirical densities. However, it is also suited to applications such as texture synthesis, where conditional densities play a central role. Results will be presented for both these applications. © 2005 ieee.

关键词： Data structures

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A novel performance evaluation method of local detectors on non-planar scenes

A novel performance evaluation method of local detectors on ...

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2005 ieee computer society conference on computer vision and pattern recognition, CVPR 2005 - Workshops

作者： Fraundorfer, Friedrich Bischof, Horst Graz University of Technology Institute for Computer Graphics and Vision Inffeldgasse 16/2 Graz8010 Austria

ISBN: (纸本)0769526608

This paper presents a new method for performance evaluation of local detectors on non-planar scenes. The framework extends the detector evaluation of Mikolajczyk and Schmid to non-planar scenes. The basic idea for establishing ground truth information on 3D scenes is the use of the trifocal tensor and the point transfer property. The repeatability and matching scores (using the SIFT descriptor) for the currently most popular local detectors are compared for increasing viewpoint change. In addition, detector combinations are evaluated by estimating the diversity of regions produced by the different detectors. The experiments reveal that the results differ from that of previous evaluations on planar scenes, especially that repeatability and matching scores will be much lower in real applications as suggested by previous evaluations. © 2005 ieee computer society. All rights reserved.

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Cross Modal Retrieval with Querybank Normalisation

Cross Modal Retrieval with Querybank Normalisation

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

作者： Bogolin, Simion-Vlad Croitoru, Ioana Jin, Hailin Liu, Yang Albanie, Samuel Univ Oxford Visual Geometry Grp Oxford England Romanian Acad Inst Math Bucharest Romania Adobe Res San Jose CA USA Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Univ Cambridge Dept Engn Cambridge England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Profiting from large-scale training datasets, advances in neural architecture design and efficient inference, joint embeddings have become the dominant approach for tackling cross-modal retrieval. In this work we first show that, despite their effectiveness, state-of-the-art joint embeddings suffer significantly from the longstanding "hubness problem" in which a small number of gallery embeddings form the nearest neighbours of many queries. Drawing inspiration from the NLP literature, we formulate a simple but effective framework called Querybank Normalisation (QB-NORM) that re-normalises query similarities to account for hubs in the embedding space. QB-NORM improves retrieval performance without requiring retraining. Differently from prior work, we show that QB-NORM works effectively without concurrent access to any test set queries. Within the QB-NORM framework, we also propose a novel similarity normalisation method, the Dynamic Inverted Softmax, that is significantly more robust than existing approaches. We showcase QB-NORM across a range of cross modal retrieval models and benchmarks where it consistently enhances strong baselines beyond the state of the art. Code is available at https://***/QB-Norm/.

关键词： Training computer vision Codes Computational modeling computer architecture Benchmark testing pattern recognition

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Joint Negative and Positive Learning for Noisy Labels

Joint Negative and Positive Learning for Noisy Labels

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

作者： Kim, Youngdong Yun, Juseung Shon, Hyounguk Kim, Junmo Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781665445092

Training of Convolutional Neural Networks (CNNs) with data with noisy labels is known to be a challenge. Based on the fact that directly providing the label to the data (Positive Learning;PL) has a risk of allowing CNNs to memorize the contaminated labels for the case of noisy data, the indirect learning approach that uses complementary labels (Negative Learning for Noisy Labels;NLNL) has proven to be highly effective in preventing overfitting to noisy data as it reduces the risk of providing faulty target. NLNL further employs a three-stage pipeline to improve convergence. As a result, filtering noisy data through the NLNL pipeline is cumbersome, increasing the training cost. In this study, we propose a novel improvement of NLNL, named Joint Negative and Positive Learning (JNPL), that unifies the filtering pipeline into a single stage. JNPL trains CNN via two losses, NL+ and PL+, which are improved upon NL and PL loss functions, respectively. We analyze the fundamental issue of NL loss function and develop new NL+ loss function producing gradient that enhances the convergence of noisy data. Furthermore, PL+ loss function is designed to enable faster convergence to expected-to-be-clean data. We show that the NL+ and PL+ train CNN simultaneously, significantly simplifying the pipeline, allowing greater ease of practical use compared to NLNL. With a simple semisupervised training technique, our method achieves stateof-the-art accuracy for noisy data classification based on the superior filtering ability.

关键词： Training computer vision Costs Filtering Pipelines Training data pattern recognition

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Learning Transferable Human-Object Interaction Detector with Natural Language Supervision

Learning Transferable Human-Object Interaction Detector with...

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

作者： Wang, Suchen Duan, Yueqi Ding, Henghui Tan, Yap-Peng Yap, Kim-Hui Yuan, Junsong Nanyang Technol Univ Singapore Singapore Tsinghua Univ Beijing Peoples R China SUNY Buffalo Buffalo NY USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

It is difficult to construct a data collection including all possible combinations of human actions and interacting objects due to the combinatorial nature of human-object interactions (HOI). In this work, we aim to develop a transferable HOI detector for unseen interactions. Existing HOI detectors often treat interactions as discrete labels and learn a classifier according to a predetermined category space. This is inherently inapt for detecting unseen interactions which are out of the predefined categories. Conversely, we treat independent HOI labels as the natural language supervision of interactions and embed them into a joint visual-and-text space to capture their correlations. More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition. Our visual encoder is instantiated as a vision Transformer with new learnable HOI tokens and a sequence parser to generate unique HOI predictions. It distills and leverages the transferable knowledge from the pretrained CLIP model to perform the zero-shot interaction detection. Experiments on two datasets, SWIG-HOI and HICO-DET, validate that our proposed method can achieve a notable mAP improvement on detecting both seen and unseen HOIs. Our code is available at https://***/scwangdyd/promting_hoi.

关键词： Visualization computer vision Correlation Codes Natural languages Detectors Data collection

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