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Discriminative invariant kernel features: A bells-and-whistles-free approach to unsupervised face recognition and pose estimation

Discriminative invariant kernel features: A bells-and-whistl...

引用

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

作者： Pal, Dipan K. Juefei-Xu, Felix Savvides, Marios Carnegie Mellon University United States

ISBN: (纸本)9781467388511

We propose an explicitly discriminative and 'simple' approach to generate invariance to nuisance transformations modeled as unitary. In practice, the approach works well to handle non-unitary transformations as well. Our theoretical results extend the reach of a recent theory of invariance to discriminative and kernelized features based on unitary kernels. As a special case, a single common framework can be used to generate subject-specific pose-invariant features for face recognition and vice-versa for pose estimation. We show that our main proposed method (DIKF) can perform well under very challenging large-scale semisynthetic face matching and pose estimation protocols with unaligned faces using no landmarking whatsoever. We additionally benchmark on CMU MPIE and outperform previous work in almost all cases on off-angle face matching while we are on par with the previous state-of-the-art on the LFW unsupervised and image-restricted protocols, without any low-level image descriptors other than raw-pixels.

关键词： Face recognition

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Fast zero-shot image tagging

Fast zero-shot image tagging

引用

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

作者： Zhang, Yang Gong, Boqing Shah, Mubarak Center for Research in Computer Vision University of Central Florida OrlandoFL32816 United States

ISBN: (纸本)9781467388511

The well-known word analogy experiments show that the recent word vectors capture fine-grained linguistic regularities in words by linear vector offsets, but it is unclear how well the simple vector offsets can encode visual regularities over words. We study a particular image-word relevance relation in this paper. Our results show that the word vectors of relevant tags for a given image rank ahead of the irrelevant tags, along a principal direction in the word vector space. Inspired by this observation, we propose to solve image tagging by estimating the principal direction for an image. Particularly, we exploit linear mappings and nonlinear deep neural networks to approximate the principal direction from an input image. We arrive at a quite versatile tagging model. It runs fast given a test image, in constant time w.r.t. the training set size. It not only gives superior performance for the conventional tagging task on the NUS-WIDE dataset, but also outperforms competitive baselines on annotating images with previously unseen tags.

关键词： Image annotation

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Monocular depth estimation using neural regression forest

Monocular depth estimation using neural regression forest

引用

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

作者： Roy, Anirban Todorovic, Sinisa Oregon State University CorvallisOR97331 United States

ISBN: (纸本)9781467388511

This paper presents a novel deep architecture, called neural regression forest (NRF), for depth estimation from a single image. NRF combines random forests and convolutional neural networks (CNNs). Scanning windows extracted from the image represent samples which are passed down the trees of NRF for predicting their depth. At every tree node, the sample is filtered with a CNN associated with that node. Results of the convolutional filtering are passed to left and right children nodes, i.e., corresponding CNNs, with a Bernoulli probability, until the leaves, where depth estimations are made. CNNs at every node are designed to have fewer parameters than seen in recent work, but their stacked processing along a path in the tree effectively amounts to a deeper CNN. NRF allows for parallelizable training of all "shallow" CNNs, and efficient enforcing of smoothness in depth estimation results. Our evaluation on the benchmark Make3D and NYUv2 datasets demonstrates that NRF outperforms the state of the art, and gracefully handles gradually decreasing training datasets.

关键词： computer vision

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Learning temporal regularity in video sequences

Learning temporal regularity in video sequences

引用

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

作者： Hasan, Mahmudul Choi, Jonghyun Neumann, Jan Roy-Chowdhury, Amit K. Davis, Larry S. UC Riverside United States Comcast Labs DC United States University of Maryland College Park United States

ISBN: (纸本)9781467388511

Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns (termed as regularity) using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build a fully convolutional feed-forward autoencoder to learn both the local features and the classifiers as an end-to-end learning framework. Our model can capture the regularities from multiple datasets. We evaluate our methods in both qualitative and quantitative ways - showing the learned regularity of videos in various aspects and demonstrating competitive performance on anomaly detection datasets as an application.

关键词： computer vision

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DeepCut: Joint subset partition and labeling for multi person pose estimation

DeepCut: Joint subset partition and labeling for multi perso...

引用

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

作者： Pishchulin, Leonid Insafutdinov, Eldar Tang, Siyu Andres, Bjoern Andriluka, Mykhaylo Gehler, Peter Schiele, Bernt Max Planck Institute for Informatics Germany Max Planck Institute for Intelligent Systems Germany Stanford University United States

ISBN: (纸本)9781467388511

This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation1.

关键词： computer vision

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VLAD3: Encoding dynamics of deep features for action recognition

VLAD3: Encoding dynamics of deep features for action recogni...

引用

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

作者： Li, Yingwei Li, Weixin Mahadevan, Vijay Vasconcelos, Nuno University of California San Diego United States Yahoo Research United States

ISBN: (纸本)9781467388511

Previous approaches to action recognition with deep features tend to process video frames only within a small temporal region, and do not model long-range dynamic information explicitly. However, such information is important for the accurate recognition of actions, especially for the discrimination of complex activities that share sub-actions, and when dealing with untrimmed videos. Here, we propose a representation, VLAD for Deep Dynamics (VLAD3), that accounts for different levels of video dynamics. It captures short-term dynamics with deep convolutional neural network features, relying on linear dynamic systems (LDS) to model medium-range dynamics. To account for long-range inhomogeneous dynamics, a VLAD descriptor is derived for the LDS and pooled over the whole video, to arrive at the final VLAD3representation. An extensive evaluation was performed on Olympic Sports, UCF101 and THUMOS15, where the use of the VLAD3representation leads to stateofthe-art results.

关键词： Deep neural networks

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Accurate image super-resolution using very deep convolutional networks

Accurate image super-resolution using very deep convolutiona...

引用

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

作者： Kim, Jiwon Lee, Jung Kwon Lee, Kyoung Mu Department of ECE ASRI Seoul National University Korea Republic of

ISBN: (纸本)9781467388511

We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (104times higher than SRCNN [6]) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.

关键词： computer vision

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Semantic filtering

Semantic filtering

引用

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

作者： Yang, Qingxiong School of Information Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781467388511

Edge-preserving image operations aim at smoothing an image without blurring the edges. Many excellent edgepreserving filtering techniques have been proposed recently to reduce the computational complexity or/and separate different scale structures. They normally adopt a userselected scale measurement to control the detail/texture smoothing. However, natural photos contain objects of different sizes which cannot be described by a single scale measurement. On the other hand, edge/contour detection/analysis is closely related to edge-preserving filtering and has achieved significant progress recently. Nevertheless, most of the state-of-the-art filtering techniques ignore the success in this area. Inspired by the fact that learning-based edge detectors/classifiers significantly outperform traditional manually-designed detectors, this paper proposes a learning-based edge-preserving filtering technique. It synergistically combines the efficiency of the recursive filter and the effectiveness of the recent edge detector for scale-aware edge-preserving filtering. Unlike previous filtering methods, the propose filter can efficiently extract subjectively-meaningful structures from natural scenes containing multiple-scale objects.

关键词： pattern recognition

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When Naïve bayes nearest neighbors meet convolutional neural networks

When Naïve bayes nearest neighbors meet convolutional neura...

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

作者： Kuzborskij, Ilja Carlucci, Fabio Maria Caputo, Barbara Sapienza Rome University Dept. of Computer Control and Management Engineering Italy Idiap Research Institute Switzerland Switzerland

ISBN: (纸本)9781467388511

Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbor (NBNN)-based classifiers have lost momentum in the community. This is because (1) such algorithms cannot use CNN activations as input features; (2) they cannot be used as final layer of CNN architectures for end-to-end training, and (3) they are generally not scalable and hence cannot handle big data. This paper proposes a framework that addresses all these issues, thus bringing back NBNNs on the map. We solve the first by extracting CNN activations from local patches at multiple scale levels, similarly to [13]. We address simultaneously the second and third by proposing a scalable version of Naive Bayes Non-linear Learning (NBNL, [7]). Results obtained using pre-trained CNNs on standard scene and domain adaptation databases show the strength of our approach, opening a new season for NBNNs.

关键词： Big data

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Regularizing Long Short Term Memory with 3D human-skeleton sequences for action recognition

Regularizing Long Short Term Memory with 3D human-skeleton s...

引用

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

作者： Mahasseni, Behrooz Todorovic, Sinisa Oregon State University CorvallisOR97331 United States

ISBN: (纸本)9781467388511

This paper argues that large-scale action recognition in video can be greatly improved by providing an additional modality in training data - namely, 3D human-skeleton sequences - aimed at complementing poorly represented or missing features of human actions in the training videos. For recognition, we use Long Short Term Memory (LSTM) grounded via a deep Convolutional Neural Network (CNN) onto the video. Training of LSTM is regularized using the output of another encoder LSTM (eLSTM) grounded on 3D human-skeleton training data. For such regularized training of LSTM, we modify the standard backpropagation through time (BPTT) in order to address the wellknown issues with gradient descent in constraint optimization. Our evaluation on three benchmark datasets - Sports-1M, HMDB-51, and UCF101 - shows accuracy improvements from 1.7% up to 14.8% relative to the state of the art.

关键词： Long short-term memory

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