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Learning aligned cross-modal representations from weakly aligned data

Learning aligned cross-modal representations from weakly ali...

引用

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

作者： Castrejón, Lluís Aytar, Yusuf Vondrick, Carl Pirsiavash, Hamed Torralba, Antonio University of Toronto Canada MIT CSAIL United States University of Maryland Baltimore County United States

ISBN: (纸本)9781467388511

People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize cross-modal scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for crossmodal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality. Our experiments suggest that our scene representation can help transfer representations across modalities for retrieval. Moreover, our visualizations suggest that units emerge in the shared representation that tend to activate on consistent concepts independently of the modality.

关键词： computer vision

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Recognizing micro-actions and reactions from paired egocentric videos

Recognizing micro-actions and reactions from paired egocentr...

引用

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

作者： Yonetani, Ryo Kitani, Kris M. Sato, Yoichi University of Tokyo Tokyo Japan Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781467388511

We aim to understand the dynamics of social interactions between two people by recognizing their actions and reactions using a head-mounted camera. Our work will impact several first-person vision tasks that need the detailed understanding of social interactions, such as automatic video summarization of group events and assistive systems. To recognize micro-level actions and reactions, such as slight shifts in attention, subtle nodding, or small hand actions, where only subtle body motion is apparent, we propose to use paired egocentric videos recorded by two interacting people. We show that the first-person and second-person points-of-view features of two people, enabled by paired egocentric videos, are complementary and essential for reliably recognizing micro-actions and reactions. We also build a new dataset of dyadic (two-persons) interactions that comprises more than 1000 pairs of egocentric videos to enable systematic evaluations on the task of micro-action and reaction recognition.

关键词： computer vision

来源：评论

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Kernel approximation via empirical orthogonal decomposition for unsupervised feature learning

Kernel approximation via empirical orthogonal decomposition ...

引用

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

作者： Mukuta, Yusuke Harada, Tatsuya University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan

ISBN: (纸本)9781467388511

Kernel approximation methods are important tools for various machine learning problems. There are two major methods used to approximate the kernel function: the Nyström method and the random features method. However, the Nyström method requires relatively high-complexity post-processing to calculate a solution and the random features method does not provide sufficient generalization performance. In this paper, we propose a method that has good generalization performance without high-complexity postprocessing via empirical orthogonal decomposition using the probability distribution estimated from training data. We provide a bound for the approximation error of the proposed method. Our experiments show that the proposed method is better than the random features method and comparable with the Nyström method in terms of the approximation error and classification accuracy. We also show that hierarchical feature extraction using our kernel approximation demonstrates better performance than the existing methods.

关键词： computer vision

来源：评论

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How far are we from solving pedestrian detection?

How far are we from solving pedestrian detection?

引用

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

作者： Zhang, Shanshan Benenson, Rodrigo Omran, Mohamed Hosang, Jan Schiele, Bernt Max Planck Institute for Informatics Saarbrücken Germany

ISBN: (纸本)9781467388511

Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the "perfect single frame detector". We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector. Our results characterise both localisation and background-versusforeground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech dataset, and provide a new sanitised set of training and test annotations.

关键词： computer vision

来源：评论

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Event-specific image importance

Event-specific image importance

引用

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

作者： Wang, Yufei Lin, Zhe Shen, Xiaohui Mch, Radomír Miller, Gavin Cottrell, Garrison W. University of California San Diego United States Adobe Research United States

ISBN: (纸本)9781467388511

When creating a photo album of an event, people typically select a few important images to keep or share. There is some consistency in the process of choosing the important images, and discarding the unimportant ones. Modeling this selection process will assist automatic photo selection and album summarization. In this paper, we show that the selection of important images is consistent among different viewers, and that this selection process is related to the event type of the album. We introduce the concept of event-specific image importance. We collected a new event album dataset with human annotation of the relative image importance with each event album. We also propose a Convolutional Neural Network (CNN) based method to predict the image importance score of a given event album, using a novel rank loss function and a progressive training scheme. Results demonstrate that our method significantly outperforms various baseline methods.

关键词： computer vision

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Temporally coherent 4D reconstruction of complex dynamic scenes

Temporally coherent 4D reconstruction of complex dynamic sce...

引用

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

作者： Mustafa, Armin Kim, Hansung Guillemaut, Jean-Yves Hilton, Adrian CVSSP University of Surrey Guildford United Kingdom

ISBN: (纸本)9781467388511

This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.

关键词： computer vision

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LOMo: Latent ordinal model for facial analysis in videos

LOMo: Latent ordinal model for facial analysis in videos

引用

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

作者： Sikka, Karan Sharma, Gaurav Bartlett, Marian UCSD Machine Perception Lab United States MPI for Informatics Germany IIT Kanpur India CSE Indian Institute of Technology Kanpur India

ISBN: (纸本)9781467388511

We study the problem of facial analysis in videos. We propose a novel weakly supervised learning method that models the video event (expression, pain etc.) as a sequence of automatically mined, discriminative sub-events (e.g. onset and offset phase for smile, brow lower and cheek raise for pain). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF - it extends such frameworks to model the ordinal or temporal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations. In combination with complimentary features, we report state-of-the-art results on these datasets.

关键词： computer vision

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Incremental object discovery in time-varying image collections

Incremental object discovery in time-varying image collectio...

引用

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

作者： Kontogianni, Theodora Mathias, Markus Leibe, Bastian Visual Computing Institute Computer Vision Group RWTH Aachen University Germany

ISBN: (纸本)9781467388511

In this paper, we address the problem of object discovery in time-varying, large-scale image collections. A core part of our approach is a novel Limited Horizon Minimum Spanning Tree (LH-MST) structure that closely approximates the Minimum Spanning Tree at a small fraction of the latter's computational cost. Our proposed tree structure can be created in a local neighborhood of the matching graph during image retrieval and can be efficiently updated whenever the image database is extended. We show how the LH-MST can be used within both single-link hierarchical agglomerative clustering and the Iconoid Shift framework for object discovery in image collections, resulting in significant efficiency gains and making both approaches capable of incremental clustering with online updates. We evaluate our approach on a dataset of 500k images from the city of Paris and compare its results to the batch version of both clustering algorithms.

关键词： Clustering algorithms

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Joint unsupervised deformable spatio-temporal alignment of sequences

Joint unsupervised deformable spatio-temporal alignment of s...

引用

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

作者： Zafeiriou, Lazaros Antonakos, Epameinondas Zafeiriou, Stefanos Pantic, Maja Imperial College London United Kingdom University of Twente Netherlands Center for Machine Vision and Signal Analysis University of Oulu Finland

ISBN: (纸本)9781467388511

Typically, the problems of spatial and temporal alignment of sequences are considered disjoint. That is, in order to align two sequences, a methodology that (non)-rigidly aligns the images is first applied, followed by temporal alignment of the obtained aligned images. In this paper, we propose the first, to the best of our knowledge, methodology that can jointly spatio-temporally align two sequences, which display highly deformable texture-varying objects. We show that by treating the problems of deformable spatial and temporal alignment jointly, we achieve better results than considering the problems independent. Furthermore, we show that deformable spatio-temporal alignment of faces can be performed in an unsupervised manner (i.e., without employing face trackers or building person-specific deformable models).

关键词： Deformation

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Robust light field depth estimation for noisy scene with occlusion

Robust light field depth estimation for noisy scene with occ...

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

作者： Williem Park, In Kyu Dept. of Information and Communication Engineering Inha University France

ISBN: (纸本)9781467388511

Light field depth estimation is an essential part of many light field applications. Numerous algorithms have been developed using various light field characteristics. However, conventional methods fail when handling noisy scene with occlusion. To remedy this problem, we present a light field depth estimation method which is more robust to occlusion and less sensitive to noise. Novel data costs using angular entropy metric and adaptive defocus response are introduced. Integration of both data costs improves the occlusion and noise invariant capability significantly. Cost volume filtering and graph cut optimization are utilized to improve the accuracy of the depth map. Experimental results confirm that the proposed method is robust and achieves high quality depth maps in various scenes. The proposed method outperforms the state-of-the-art light field depth estimation methods in qualitative and quantitative evaluation.

关键词： computer vision

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