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A multi-level contextual model for person recognition in photo albums

A multi-level contextual model for person recognition in pho...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Li, Haoxiang Brandt, Jonathan Lin, Zhe Shen, Xiaohui Hua, Gang Stevens Institute of Technology United States Adobe Research United States Microsoft Research United States

ISBN: (纸本)9781467388511

In this work, we present a new framework for person recognition in photo albums that exploits contextual cues at multiple levels, spanning individual persons, individual photos, and photo groups. Through experiments, we show that the information available at each of these distinct contextual levels provides complementary cues as to person identities. At the person level, we leverage clothing and body appearance in addition to facial appearance, and to compensate for instances where the faces are not visible. At the photo level we leverage a learned prior on the joint distribution of identities on the same photo to guide the identity assignments. Going beyond a single photo, we are able to infer natural groupings of photos with shared context in an unsupervised manner. By exploiting this shared contextual information, we are able to reduce the identity search space and exploit higher intra-personal appearance consistency within photo groups. Our new framework enables efficient use of these complementary multi-level contextual cues to improve overall recognition rates on the photo album person recognition task, as demonstrated through state-of-theart results on a challenging public dataset. Our results outperform competing methods by a significant margin, while being computationally efficient and practical in a real world application.

关键词： Image enhancement

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Ask me anything: Free-form visual question answering based on knowledge from external sources

Ask me anything: Free-form visual question answering based o...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wu, Qi Wang, Peng Shen, Chunhua Dick, Anthony Van Den Hengel, Anton School of Computer Science University of Adelaide Australia

ISBN: (纸本)9781467388511

We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. This allows more complex questions to be answered using the predominant neural network-based approach than has previously been possible. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain the whole answer. The method constructs a textual representation of the semantic content of an image, and merges it with textual information sourced from a knowledge base, to develop a deeper understanding of the scene viewed. Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach. We are specifically able to answer questions posed in natural language, that refer to information not contained in the image. We demonstrate the effectiveness of our model on two publicly available datasets, Toronto COCO-QA [23] and VQA [1] and show that it produces the best reported results in both cases.

关键词： Natural language processing systems

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Tensor power iteration for multi-graph matching

Tensor power iteration for multi-graph matching

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Shi, Xinchu Ling, Haibin Hu, Weiming Xing, Junliang Zhang, Yanning CAS Center for Excellence in Brain Science and Intelligence Technology National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States School of Computer Science Northwestern Polytechnical University Xian China

ISBN: (纸本)9781467388511

Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic. By contrast, it is still under-exploited on how to jointly match multiple graphs, partly due to its intrinsic combinatorial intractability. In this work, we address this challenging problem in a principled way under the rank-1 tensor approximation framework. In particular, we formulate multi-graph matching as a combinational optimization problem with two main ingredients: unary matching over graph vertices and structure matching over graph edges, both of which across multiple graphs. Then we propose an efficient power iteration solution for the resulting NP-hard optimization problem. The proposed algorithm has several advantages: 1) the intrinsic matching consistency across multiple graphs based on the high-order tensor optimization; 2) the free employment of powerful high-order node affinity; 3) the flexible integration between various types of node affinities and edge/hyper-edge affinities. Experiments on diverse and challenging datasets validate the effectiveness of the proposed approach in comparison with state-ofthe-arts.

关键词： Graph theory

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Sparse to dense 3D reconstruction from rolling shutter images

Sparse to dense 3D reconstruction from rolling shutter image...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Saurer, Olivier Pollefeys, Marc Lee, Gim Hee Department of Computer Science ETH Zurich Switzerland Department of Mechanical Engineering National University of Singapore Singapore

ISBN: (纸本)9781467388511

It is well known that the rolling shutter effect in images captured with a moving rolling shutter camera causes inaccuracies to 3D reconstructions. The problem is further aggravated with weak visual connectivity from wide baseline images captured with a fast moving camera. In this paper, we propose and implement a pipeline for sparse to dense 3D construction with wide baseline images captured from a fast moving rolling shutter camera. Specifically, we propose a cost function for Bundle Adjustment (BA) that models the rolling shutter effect, incorporates GPS/INS readings, and enforces pairwise smoothness between neighboring poses. We optimize over the 3D structures, camera poses and velocities. We also introduce a novel interpolation scheme for the rolling shutter plane sweep stereo algorithm that allows us to achieve a 7× speed up in the depth map computations for dense reconstruction without losing accuracy. We evaluate our proposed pipeline over a 2.6km image sequence captured with a rolling shutter camera mounted on a moving car.

关键词： Image reconstruction

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Object tracking via dual linear structured SVM and explicit feature map

Object tracking via dual linear structured SVM and explicit ...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Ning, Jifeng Yang, Jimei Jiang, Shaojie Zhang, Lei Yang, Ming-Hsuan College of Information Engineering Northwest A and F University China Adobe Research United States Department of Computing Hong Kong Polytechnic University China Electrical Engineering and Computer Science University of California Merced United States

ISBN: (纸本)9781467388511

Structured support vector machine (SSVM) based methods have demonstrated encouraging performance in recent object tracking benchmarks. However, the complex and expensive optimization limits their deployment in real-world applications. In this paper, we present a simple yet efficient dual linear SSVM (DLSSVM) algorithm to enable fast learning and execution during tracking. By analyzing the dual variables, we propose a primal classifier update formula where the learning step size is computed in closed form. This online learning method significantly improves the robustness of the proposed linear SSVM with lower computational cost. Second, we approximate the intersection kernel for feature representations with an explicit feature map to further improve tracking performance. Finally, we extend the proposed DLSSVM tracker with multi-scale estimation to address the "drift" problem. Experimental results on large benchmark datasets with 50 and 100 video sequences show that the proposed DLSSVM tracking algorithm achieves state-of-the-art performance.

关键词： Support vector machines

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Using spatial order to boost the elimination of incorrect feature matches

Using spatial order to boost the elimination of incorrect fe...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Talker, Lior Moses, Yael Shimshoni, Ilan University of Haifa Herzliya46150 Israel Interdisciplinary Center Herzliya46150 Israel

ISBN: (纸本)9781467388511

Correctly matching feature points in a pair of images is an important preprocessing step for many computer vision applications. In this paper we propose an efficient method for estimating the number of correct matches without explicitly computing them. In addition, our method estimates the region of overlap between the images. To this end, we propose to analyze the set of matches using the spatial order of the features, as projected to the x-axis of the image. The set of features in each image is thus represented by a sequence. This reduces the analysis of the matching problem to the analysis of the permutation between the sequences. Using the Kendall distance metric between permutations and natural assumptions on the distribution of the correct and incorrect matches, we show how to estimate the abovementioned values. We demonstrate the usefulness of our method in two applications: (i) a new halting condition for RANSAC based epipolar geometry estimation methods that considerably reduce the running time, and (ii) discarding spatially unrelated image pairs in the Structure-from-Motion pipeline. Furthermore, our analysis can be used to compute the probability that a given match is correct based on the estimated number of correct matches and the rank of the features within the sequences. Our experiments on a large number of synthetic and real data demonstrate the effectiveness of our method. For example, the running time of the image matching stage in the Structure-from-Motion pipeline may be reduced by about 99% while preserving about 80% of the correctly matched feature points.

关键词： computer vision

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A continuous occlusion model for road scene understanding

A continuous occlusion model for road scene understanding

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Dhiman, Vikas Tran, Quoc-Huy Corso, Jason J. Chandraker, Manmohan University of Michigan Ann ArborMI United States NEC Laboratories America Inc. CupertinoCA United States

ISBN: (纸本)9781467388511

We present a physically interpretable, continuous threedimensional (3D) model for handling occlusions with applications to road scene understanding. We probabilistically assign each point in space to an object with a theoretical modeling of the reflection and transmission probabilities for the corresponding camera ray. Our modeling is unified in handling occlusions across a variety of scenarios, such as associating structure from motion (SFM) point tracks with potentially occluding objects or modeling object detection scores in applications such as 3D localization. For point track association, our model uniformly handles static and dynamic objects, which is an advantage over motion segmentation approaches traditionally used in multibody SFM. Detailed experiments on the KITTI raw dataset show the superiority of the proposed method over both state-of-the-art motion segmentation and a baseline that heuristically uses detection bounding boxes for resolving occlusions. We also demonstrate how our continuous occlusion model may be applied to the task of 3D localization in road scenes.

关键词： Three dimensional computer graphics

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Two illuminant estimation and user correction preference

Two illuminant estimation and user correction preference

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Cheng, Dongliang Kamel, Abdelrahman Price, Brian Cohen, Scott Brown, Michael S. National University of Singapore Singapore Adobe Research United States

ISBN: (纸本)9781467388511

This paper examines the problem of white-balance correction when a scene contains two illuminations. This is a two step process: 1) estimate the two illuminants; and 2) correct the image. Existing methods attempt to estimate a spatially varying illumination map, however, results are error prone and the resulting illumination maps are too lowresolution to be used for proper spatially varying whitebalance correction. In addition, the spatially varying nature of these methods make them computationally intensive. We show that this problem can be effectively addressed by not attempting to obtain a spatially varying illumination map, but instead by performing illumination estimation on large sub-regions of the image. Our approach is able to detect when distinct illuminations are present in the image and accurately measure these illuminants. Since our proposed strategy is not suitable for spatially varying image correction, a user study is performed to see if there is a preference for how the image should be corrected when two illuminants are present, but only a global correction can be applied. The user study shows that when the illuminations are distinct, there is a preference for the outdoor illumination to be corrected resulting in warmer final result. We use these collective findings to demonstrate an effective two illuminant estimation scheme that produces corrected images that users prefer.

关键词： computer vision

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Reinforcement learning for visual object detection

Reinforcement learning for visual object detection

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Mathe, Stefan Pirinen, Aleksis Sminchisescu, Cristian Department of Mathematics Faculty of Engineering Lund University Sweden Institute of Mathematics Romanian Academy Romania Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have been successful and effective for many years, they are still brute-force, independent of the image content and the visual category being searched. In this paper we present principled sequential models that accumulate evidence collected at a small set of image locations in order to detect visual objects effectively. By formulating sequential search as reinforcement learning of the search policy (including the stopping condition), our fully trainable model can explicitly balance for each class, specifically, the conflicting goals of exploration - sampling more image regions for better accuracy-, and exploitation - stopping the search efficiently when sufficiently confident about the target's location. The methodology is general and applicable to any detector response function. We report encouraging results in the PASCAL VOC 2012 object detection test set showing that the proposed methodology achieves almost two orders of magnitude speed-up over sliding window methods.

关键词： Object detection

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Learned binary spectral shape descriptor for 3D shape correspondence

Learned binary spectral shape descriptor for 3D shape corres...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Xie, Jin Wang, Meng Fang, Yi NYU Multimedia and Visual Computing Lab United Arab Emirates Department of Electrical and Computer Engineering New York University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering NYU Tandon School of Engineering United States

ISBN: (纸本)9781467388511

Dense 3D shape correspondence is an important problem in computer vision and computer graphics. Recently, the local shape descriptor based 3D shape correspondence approaches have been widely studied, where the local shape descriptor is a real-valued vector to characterize the geometrical structure of the shape. Different from these realvalued local shape descriptors, in this paper, we propose to learn a novel binary spectral shape descriptor with the deep neural network for 3D shape correspondence. The binary spectral shape descriptor can require less storage space and enable fast matching. First, based on the eigenvectors of the Laplace-Beltrami operator, we construct a neural network to form a nonlinear spectral representation to characterize the shape. Then, for the defined positive and negative points on the shapes, we train the constructed neural network by minimizing the errors between the outputs and their corresponding binary descriptors, minimizing the variations of the outputs of the positive points and maximizing the variations of the outputs of the negative points, simultaneously. Finally, we binarize the output of the neural network to form the binary spectral shape descriptor for shape correspondence. The proposed binary spectral shape descriptor is evaluated on the SCAPE and TOSCA 3D shape datasets for shape correspondence. The experimental results demonstrate the effectiveness of the proposed binary shape descriptor for the shape correspondence task.

关键词： Object recognition

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