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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是371-380 订阅

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Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks 30

Unsupervised Adaptive Re-identification in Open World Dynami...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Panda, Rameswar Bhuiyan, Amran Murino, Vittorio Roy-Chowdhury, Amit K. UC Riverside Dept ECE Riverside CA 92521 USA Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy

ISBN: (纸本)9781538604571

Person re-identification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may be temporarily inserted into an existing system to get additional information. To address such a novel and very practical problem, we propose an unsupervised adaptation scheme for re-identification models in a dynamic camera network. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with a newly introduced target camera, without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Extensive experiments on four benchmark datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised learning based alternatives whilst being extremely efficient to compute.

关键词： Cameras images computer vision Inference algorithms

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Lean Crowdsourcing: Combining Humans and Machines in an Online System 30

Lean Crowdsourcing: Combining Humans and Machines in an Onli...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Branson, Steve Van Horn, Grant Perona, Pietro CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9781538604571

We introduce a method to greatly reduce the amount of redundant annotations required when crowdsourcing annotations such as bounding boxes, parts, and class labels. For example, if two Mechanical Turkers happen to click on the same pixel location when annotating a part in a given image-an event that is very unlikely to occur by random chance-, it is a strong indication that the location is correct. A similar type of confidence can be obtained if a single Turker happened to agree with a computer vision estimate. We thus incrementally collect a variable number of worker annotations per image based on online estimates of confidence. this is done using a sequential estimation of risk over a probabilistic model that combines worker skill, image difficulty, and an incrementally trained computer vision model. We develop specialized models and algorithms for binary annotation, part keypoint annotation, and sets of bounding box annotations. We show that our method can reduce annotation time by a factor of 4-11 for binary filtering of websearch results, 2-4 for annotation of boxes of pedestrians in images, while in many cases also reducing annotation error. We will make an end-to-end version of our system publicly available.

关键词： computer vision Crowdsourcing Computational modeling Prediction algorithms Tools Training Noise measurement

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Fine-Grained recognition as HSnet Search for Informative Image Parts 30

Fine-Grained Recognition as HSnet Search for Informative Ima...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lam, Michael Mahasseni, Behrooz Todorovic, Sinisa Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781538604571

this work addresses fine-grained image classification. Our work is based on the hypothesis that when dealing with subtle differences among object classes it is critical to identify and only account for a few informative image parts, as the remaining image context may not only be uninformative but may also hurt recognition. this motivates us to formulate our problem as a sequential search for informative parts over a deep feature map produced by a deep Convolutional Neural Network (CNN). A state of this search is a set of proposal bounding boxes in the image, whose "informativeness" is evaluated by the heuristic function (H), and used for generating new candidate states by the successor function (S). the two functions are unified via a Long Short-Term Memory network (LSTM) into a new deep recurrent architecture, called HSnet. thus, HSnet (i) generates proposals of informative image parts and (ii) fuses all proposals toward final fine-grained recognition. We specify both supervised and weakly supervised training of HSnet depending on the availability of object part annotations. Evaluation on the benchmark Caltech-UCSD Birds 2002011 and Cars-196 datasets demonstrate our competitive performance relative to the state of the art.

关键词： Image recognition Proposals Search problems computer architecture Object recognition Trajectory Feature extraction

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Compact Matrix Factorization with Dependent Subspaces 30

Compact Matrix Factorization with Dependent Subspaces

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Larsson, Viktor Olsson, Carl Lund Univ Ctr Math Sci Lund Sweden Chalmers Univ Technol Dept Signals & Syst Gothenburg Sweden

ISBN: (纸本)9781538604571

Traditional matrix factorization methods approximate high dimensional data with a low dimensional subspace. this imposes constraints on the matrix elements which allow for estimation of missing entries. A lower rank provides stronger constraints and makes estimation of the missing entries less ambiguous at the cost of measurement fit. In this paper we propose a new factorization model that further constrains the matrix entries. Our approach can be seen as a unification of traditional low-rank matrix factorization and the more recent union-of-subspace approach. It adaptively finds clusters that can be modeled with low dimensional local subspaces and simultaneously uses a global rank constraint to capture the overall scene interactions. For inference we use an energy that penalizes a trade-off between data fit and degrees-of-freedom of the resulting factorization. We show qualitatively and quantitatively that regularizing both local and global dynamics yields significantly improved missing data estimation.

关键词： Computational modeling Data models Adaptation models Estimation Matrix decomposition Predictive models computer vision

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Temporal Residual Networks for Dynamic Scene recognition 30

Temporal Residual Networks for Dynamic Scene Recognition

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Feichtenhofer, Christoph Pinz, Axel Wildes, Richard P. Graz Univ Technol Graz Austria York Univ Toronto ON Canada

ISBN: (纸本)9781538604571

this paper combines three contributions to establish a new state-of-the-art in dynamic scene recognition. First, we present a novel ConvNet architecture based on temporal residual units that is fully convolutional in spacetime. Our model augments spatial ResNets with convolutions across time to hierarchically add temporal residuals as the depth of the network increases. Second, existing approaches to video-based recognition are categorized and a baseline of seven previously top performing algorithms is selected for comparative evaluation on dynamic scenes. third, we introduce a new and challenging video database of dynamic scenes that more than doubles the size of those previously available. this dataset is explicitly split into two subsets of equal size that contain videos with and without camera motion to allow for systematic study of how this variable interacts with the defining dynamics of the scene per se. Our evaluations verify the particular strengths and weaknesses of the baseline algorithms with respect to various scene classes and camera motion parameters. Finally, our temporal ResNet boosts recognition performance and establishes a new state-of-the-art on dynamic scene recognition, as well as on the complementary task of action recognition.

关键词： recognition (Psychology) video databases Spacetime Videotapes Arts RESIDUAL Cameras BASELINE

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Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications 30

Joint Graph Decomposition & Node Labeling: Problem, Algorith...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Levinkov, Evgeny Uhrig, Jonas Tang, Siyu Omran, Mohamed Insafutdinov, Eldar Kirillov, Alexander Rother, Carsten Brox, thomas Schiele, Bernt Andres, Bjoern Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany Max Planck Inst Intelligent Syst Tubingen Germany Daimler AG R&D Sindelfingen Germany Univ Freiburg Comp Vis Lab Freiburg Germany Tech Univ Dresden Comp Vis Lab Dresden Germany

ISBN: (纸本)9781538604571

We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. this problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, the problem we state generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time. To demonstrate their effectiveness in tackling computer vision tasks, we apply these algorithms to instances of the problem that we construct from published data, using published algorithms. We report state-of-the-art application-specific accuracy for the three above-mentioned applications.

关键词： Labeling Cost function computer vision Object tracking Semantics

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Efficient Multiple Instance Metric Learning using Weakly Supervised Data 30

Efficient Multiple Instance Metric Learning using Weakly Sup...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Law, Marc T. Yu, Yaoliang Urtasun, Raquel Zemel, Richard S. Xing, Eric P. Univ Toronto Toronto ON Canada Univ Waterloo Waterloo ON Canada Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781538604571

We consider learning a distance metric in a weakly supervised setting where "bags" (or sets) of instances are labeled with "bags" of labels. A general approach is to formulate the problem as a Multiple Instance Learning (MIL) problem where the metric is learned so that the distances between instances inferred to be similar are smaller than the distances between instances inferred to be dissimilar. Classic approaches alternate the optimization over the learned metric and the assignment of similar instances. In this paper, we propose an efficient method that jointly learns the metric and the assignment of instances. In particular, our model is learned by solving an extension of kmeans for MIL problems where instances are assigned to categories depending on annotations provided at bag-level. Our learning algorithm is much faster than existing metric learning methods for MIL problems and obtains state-of-the-art recognition performance in automated image annotation and instance classification for face identification.

关键词： Metric Metronidazole ANNOTATIONS Facial recognition distance metric learning algorithms PURSE

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A Domain Based Approach to Social Relation recognition 30

A Domain Based Approach to Social Relation Recognition

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sun, Qianru Schiele, Bernt Fritz, Mario Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781538604571

Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domain-based theory from social psychology is a great starting point to systematically approach this problem. the theory provides coverage of all aspects of social relations and equally is concrete and predictive about the visual attributes and behaviors defining the relations included in each domain. We provide the first dataset built on this holistic conceptualization of social life that is composed of a hierarchical label space of social domains and social relations. We also contribute the first models to recognize such domains and relations and find superior performance for attribute based features. Beyond the encouraging performance of the attribute based approach, we also find interpretable features that are in accordance with the predictions from social psychology literature. Beyond our findings, we believe that our contributions more tightly interleave visual recognition and social psychology theory that has the potential to complement the theoretical work in the area with empirical and data-driven models of social life.

关键词： Psychology Computational modeling Concrete Visualization Face Semantics computer vision

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Automatic Understanding of Image and Video Advertisements 30

Automatic Understanding of Image and Video Advertisements

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hussain, Zaeem Zhang, Mingda Zhang, Xiaozhong Ye, Keren thomas, Christopher Agha, Zuha Ong, Nathan Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781538604571

there is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action. We propose the novel problem of automatic advertisement understanding. To enable research on this problem, we create two datasets: an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. Our data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer ("What should I do according to this ad, and why should I do it?"), and symbolic references ads make (e.g. a dove symbolizes peace). We also analyze the most common persuasive strategies ads use, and the capabilities that computer vision systems should have to understand these strategies. We present baseline classification results for several prediction tasks, including automatically answering questions about the messages of the ads.

关键词： Decoding Visualization Rhetoric computer vision Animals Streaming media images Streaming Video Rhetoric computer vision Viewer Device Component DECODING Visualization Advertisements Image AD computer vision Systems Animals AUTOMATIC Dataset Imagery (Psychotherapy)

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Learning Cross-modal Embeddings for Cooking Recipes and Food Images 30

Learning Cross-modal Embeddings for Cooking Recipes and Food...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Salvador, Amaia Hynes, Nicholas Aytar, Yusuf Marin, Javier Ofli, Ferda Weber, Ingmar Torralba, Antonio Univ Politecn Cataluna Barcelona Spain MIT Cambridge MA 02139 USA HBKU Qatar Comp Res Inst Doha Qatar

ISBN: (纸本)9781538604571

In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images. As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high-capacity models on aligned, multi-modal data. Using these data, we train a neural network to find a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Additionally, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M dataset and food and cooking in general. Code, data and models are publicly available(1).

关键词： Training Image representation Semantics computer vision Data models Tools

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