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Functional faces: Groupwise dense correspondence using functional maps

Functional faces: Groupwise dense correspondence using funct...

引用

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

作者： Zhang, Chao Smith, William A. P. Dessein, Arnaud Pears, Nick Dai, Hang Department of Computer Science University of York United Kingdom IMB LaBRI Université de Bordeaux France

ISBN: (纸本)9781467388511

In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps. The functional maps paradigm brings with it a number of advantages for face correspondence. First, it allows us to combine various notions of correspondence. We do so by proposing a number of face-specific functions, suited to either within- or between-subject correspondence. Second, we propose a groupwise variant of the method allowing us to compute cycle-consistent functional maps between all faces in a training set. Since functional maps are of much lower dimension than point-to-point correspondences, this is feasible even when the input meshes are very high resolution. Finally, we show how a functional map provides a geometric constraint that can be used to filter feature matches between non-rigidly deforming surfaces.

关键词： computer vision

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BORDER: An oriented rectangles approach to texture-less object recognition

BORDER: An oriented rectangles approach to texture-less obje...

引用

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

作者： Chan, Jacob Lee, Jimmy Addison Kemao, Qian Nanyang Technological University Block N4 Nanyang Avenue Singapore639798 Singapore Singapore138632 Singapore

ISBN: (纸本)9781467388511

This paper presents an algorithm coined BORDER (Bounding Oriented-Rectangle Descriptors for Enclosed Regions) for texture-less object recognition. By fusing a regional object encompassment concept with descriptor-based pipelines, we extend local-patches into scalable object-sized oriented rectangles for optimal object information encapsulation with minimal outliers. We correspondingly introduce a modified line-segment detection technique termed Linelets to stabilize keypoint repeatability in homogenous conditions. In addition, a unique sampling technique facilitates the incorporation of robust angle primitives to produce discriminative rotation-invariant descriptors. BORDER's high competence in object recognition particularly excels in homogenous conditions obtaining superior detection rates in the presence of high-clutter, occlusion and scale-rotation changes when compared with modern state-of-the-art texture-less object detectors such as BOLD and LINE2D on public texture-less object databases.

关键词： Object recognition

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Image captioning with semantic attention

Image captioning with semantic attention

引用

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

作者： You, Quanzeng Jin, Hailin Wang, Zhaowen Fang, Chen Luo, Jiebo Department of Computer Science University of Rochester RochesterNY14627 United States Adobe Research 345 Park Ave San JoseCA95110 United States

ISBN: (纸本)9781467388511

Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing. Existing approaches are either top-down, which start from a gist of an image and convert it into words, or bottom-up, which come up with words describing various aspects of an image and then combine them. In this paper, we propose a new algorithm that combines both approaches through a model of semantic attention. Our algorithm learns to selectively attend to semantic concept proposals and fuse them into hidden states and outputs of recurrent neural networks. The selection and fusion form a feedback connecting the top-down and bottom-up computation. We evaluate our algorithm on two public benchmarks: Microsoft COCO and Flickr30K. Experimental results show that our algorithm significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.

关键词： computer vision

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Pairwise decomposition of image sequences for active multi-view recognition

Pairwise decomposition of image sequences for active multi-v...

引用

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

作者： Johns, Edward Leutenegger, Stefan Davison, Andrew J. Dyson Robotics Laboratory at Imperial College Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9781467388511

A multi-view image sequence provides a much richer capacity for object recognition than from a single image. However, most existing solutions to multi-view recognition typically adopt hand-crafted, model-based geometric methods, which do not readily embrace recent trends in deep learning. We propose to bring Convolutional Neural Networks to generic multi-view recognition, by decomposing an image sequence into a set of image pairs, classifying each pair independently, and then learning an object classifier by weighting the contribution of each pair. This allows for recognition over arbitrary camera trajectories, without requiring explicit training over the potentially infinite number of camera paths and lengths. Building these pairwise relationships then naturally extends to the next-best-view problem in an active recognition framework. To achieve this, we train a second Convolutional Neural Network to map directly from an observed image to next viewpoint. Finally, we incorporate this into a trajectory optimisation task, whereby the best recognition confidence is sought for a given trajectory length. We present state-of-the-art results in both guided and unguided multi-view recognition on the ModelNet dataset, and show how our method can be used with depth images, greyscale images, or both.

关键词： computer vision

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Zero-shot learning via joint latent similarity embedding

Zero-shot learning via joint latent similarity embedding

引用

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

作者： Zhang, Ziming Saligrama, Venkatesh Department of Electrical and Computer Engineering Boston University United States

ISBN: (纸本)9781467388511

Zero-shot recognition (ZSR) deals with the problem of predicting class labels for target domain instances based on source domain side information (e.g. attributes) of unseen classes. We formulate ZSR as a binary prediction problem. Our resulting classifier is class-independent. It takes an arbitrary pair of source and target domain instances as input and predicts whether or not they come from the same class, i.e. whether there is a match. We model the posterior probability of a match since it is a sufficient statistic and propose a latent probabilistic model in this context. We develop a joint discriminative learning framework based on dictionary learning to jointly learn the parameters of our model for both domains, which ultimately leads to our class-independent classifier. Many of the existing embedding methods can be viewed as special cases of our probabilistic model. On ZSR our method shows 4.90% improvement over the state-of-the-art in accuracy averaged across four benchmark datasets. We also adapt ZSR method for zero-shot retrieval and show 22.45% improvement accordingly in mean average precision (mAP).

关键词： computer vision

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BoxCars: 3D boxes as CNN input for improved fine-grained vehicle recognition

BoxCars: 3D boxes as CNN input for improved fine-grained veh...

引用

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

作者： Sochor, Jakub Herout, Adam Havel, Jií Graph at FIT Brno University of Technology Brno Czech Republic

ISBN: (纸本)9781467388511

We are dealing with the problem of fine-grained vehicle make & model recognition and verification. Our contribution is showing that extracting additional data from the video stream - besides the vehicle image itself - and feeding it into the deep convolutional neural network boosts the recognition performance considerably. This additional information includes: 3D vehicle bounding box used for "unpacking" the vehicle image, its rasterized low-resolution shape, and information about the 3D vehicle orientation. Experiments show that adding such information decreases classification error by 26 % (the accuracy is improved from 0.772 to 0.832) and boosts verification average precision by 208% (0.378 to 0.785) compared to baseline pure CNN without any input modifications. Also, the pure baseline CNN outperforms the recent state of the art solution by 0.081. We provide an annotated set "BoxCars" of surveillance vehicle images augmented by various automatically extracted auxiliary information. Our approach and the dataset can considerably improve the performance of traffic surveillance systems.

关键词： Vehicles

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Multi-cue zero-shot learning with strong supervision

Multi-cue zero-shot learning with strong supervision

引用

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

作者： Akata, Zeynep Malinowski, Mateusz Fritz, Mario Schiele, Bernt Max-Planck Institute for Informatics United States

ISBN: (纸本)9781467388511

Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes. Ultimately, this may allow to use textbook knowledge that humans employ to learn about new classes by transferring knowledge from classes they know well. The most successful zero-shot learning approaches currently require a particular type of auxiliary information - namely attribute annotations performed by humans - that is not readily available for most classes. Our goal is to circumvent this bottleneck by substituting such annotations by extracting multiple pieces of information from multiple unstructured text sources readily available on the web. To compensate for the weaker form of auxiliary information, we incorporate stronger supervision in the form of semantic part annotations on the classes from which we transfer knowledge. We achieve our goal by a joint embedding framework that maps multiple text parts as well as multiple semantic parts into a common space. Our results consistently and significantly improve on the state-of-the-art in zero-short recognition and retrieval.

关键词： computer vision

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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

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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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