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RAID-G: Robust estimation of approximate infinite dimensional gaussian with application to material recognition

RAID-G: Robust estimation of approximate infinite dimensiona...

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

作者： Wang, Qilong Li, Peihua Zuo, Wangmeng Zhang, Lei Dalian University of Technology China Harbin Institute of Technology China Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781467388511

Infinite dimensional covariance descriptors can provide richer and more discriminative information than their low dimensional counterparts. In this paper, we propose a novel image descriptor, namely, robust approximate infinite dimensional Gaussian (RAID-G). The challenges of RAID-G mainly lie on two aspects: (1) description of infinite dimensional Gaussian is difficult due to its non-linear Riemannian geometric structure and the infinite dimensional setting, hence effective approximation is necessary; (2) traditional maximum likelihood estimation (MLE) is not robust to high (even infinite) dimensional covariance matrix in Gaussian setting. To address these challenges, explicit feature mapping (EFM) is first introduced for effective approximation of infinite dimensional Gaussian induced by additive kernel function, and then a new regularized MLE method based on von Neumann divergence is proposed for robust estimation of covariance matrix. The EFM and proposed regularized MLE allow a closed-form of RAID-G, which is very efficient and effective for high dimensional features. We extend RAID-G by using the outputs of deep convolutional neural networks as original features, and apply it to material recognition. Our approach is evaluated on five material benchmarks and one fine-grained benchmark. It achieves 84.9% accuracy on FMD and 86.3% accuracy on UIUC material database, which are much higher than state-of-the-arts.

关键词： pattern recognition

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SPDA-CNN: Unifying semantic part detection and abstracion for fine-grained recognition

SPDA-CNN: Unifying semantic part detection and abstracion fo...

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

作者： Zhang, Han Xu, Tao Elhoseiny, Mohamed Huang, Xiaolei Zhang, Shaoting Elgammal, Ahmed Metaxas, Dimitris Department of Computer Science Rutgers University United States Department of Computer Science and Engineering Lehigh University United States Department of Computer Science University of North Carolina at Charlotte United States

ISBN: (纸本)9781467388511

Most convolutional neural networks (CNNs) lack midlevel layers that model semantic parts of objects. This limits CNN-based methods from reaching their full potential in detecting and utilizing small semantic parts in recognition. Introducing such mid-level layers can facilitate the extraction of part-specific features which can be utilized for better recognition performance. This is particularly important in the domain of fine-grained recognition. In this paper, we propose a new CNN architecture that integrates semantic part detection and abstraction (SPDA-CNN) for fine-grained classification. The proposed network has two sub-networks: one for detection and one for recognition. The detection sub-network has a novel top-down proposal method to generate small semantic part candidates for detection. The classification sub-network introduces novel part layers that extract features from parts detected by the detection sub-network, and combine them for recognition. As a result, the proposed architecture provides an end-to-end network that performs detection, localization of multiple semantic parts, and whole object recognition within one framework that shares the computation of convolutional filters. Our method outperforms state-of-theart methods with a large margin for small parts detection (e.g. our precision of 93.40% vs the best previous precision of 74.00% for detecting the head on CUB-2011). It also compares favorably to the existing state-of-the-art on finegrained classification, e.g. it achieves 85.14% accuracy on CUB-2011.

关键词： Network architecture

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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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27th ieee conference on computer vision and pattern recognition, cvpr 2014

ISBN: (纸本)9781479951178

The proceedings contain 539 papers. The topics discussed include: fast and accurate image matching with cascade hashing for 3D reconstruction;minimal solvers for relative pose with a single unknown radial distortion;spectral graph reduction for efficient image and streaming video segmentation;video motion segmentation using new adaptive manifold denoising model;event detection using multi-level relevance labels and multiple features;full-angle quaternions for robustly matching vectors of 3D rotations;semi-supervised spectral clustering for image set classification;learning mid-level filters for person re-identification;DeepReID: deep filter pairing neural network for person re-identification;NMF-KNN: image annotation using weighted multi-view non-negative matrix factorization;beyond comparing image pairs: setwise active learning for relative attributes;and histograms of pattern sets for image classification and object recognition.

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Less is more: Zero-shot learning from online textual documents with noise suppression

Less is more: Zero-shot learning from online textual documen...

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

作者： Qiao, Ruizhi Liu, Lingqiao Shen, Chunhua Van Den Hengel, Anton School of Computer Science University of Adelaide Australia

ISBN: (纸本)9781467388511

Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes. Recent work has pursued this approach by exploring various ways of connecting the visual and text domains. In this paper, we revisit this idea by going further to consider one important factor: the textual representation is usually too noisy for the zero-shot learning application. This observation motivates us to design a simple yet effective zero-shot learning method that is capable of suppressing noise in the text. Specifically, we propose an l2,1-norm based objective function which can simultaneously suppress the noisy signal in the text and learn a function to match the text document and visual features. We also develop an optimization algorithm to efficiently solve the resulting problem. By conducting experiments on two large datasets, we demonstrate that the proposed method significantly outperforms those competing methods which rely on online information sources but with no explicit noise suppression. Furthermore, we make an in-depth analysis of the proposed method and provide insight as to what kind of information in documents is useful for zero-shot learning.

关键词： computer aided instruction

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Face alignment across large poses: A 3D solution

Face alignment across large poses: A 3D solution

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

作者： Zhu, Xiangyu Lei, Zhen Liu, Xiaoming Shi, Hailin Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China Department of Computer Science and Engineering Michigan State University United States

ISBN: (纸本)9781467388511

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45°), lacking the ability to align faces in large poses up to 90°. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.

关键词： Alignment

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Copula ordinal regression for joint estimation of facial action unit intensity

Copula ordinal regression for joint estimation of facial act...

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

作者： Walecki, Robert Rudovic, Ognjen Pavlovic, Vladimir Pantic, Maja Department of Computing Imperial College London United Kingdom Department of Computer Science Rutgers University United States EEMCS University of Twente Netherlands

ISBN: (纸本)9781467388511

Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6). This is in part due to the lack of suitable models that can efficiently handle such a large number of outputs/classes simultaneously, but also due to the lack of labelled target data. For this reason, majority of the methods proposed so far resort to independent classifiers for the AU intensity. This is suboptimal for at least two reasons: the facial appearance of some AUs changes depending on the intensity of other AUs, and some AUs co-occur more often than others. Encoding this is expected to improve the estimation of target AU intensities, especially in the case of noisy image features, head-pose variations and imbalanced training data. To this end, we introduce a novel modeling framework, Copula Ordinal Regression (COR), that leverages the power of copula functions and CRFs, to detangle the probabilistic modeling of AU dependencies from the marginal modeling of the AU intensity. Consequently, the COR model achieves the joint learning and inference of intensities of multiple AUs, while being computationally tractable. We show on two challenging datasets of naturalistic facial expressions that the proposed approach consistently outperforms (i) independent modeling of AU intensities, and (ii) the state-ofthe-art approach for the target task.

关键词： computer vision

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Deep residual learning for image recognition

Deep residual learning for image recognition

引用

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

作者： He, Kaiming Zhang, Xiangyu Ren, Shaoqing Sun, Jian Microsoft Research United States

ISBN: (纸本)9781467388511

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers-8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

关键词： Deep learning

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Amplitude Modulated video camera - Light separation in dynamic scenes

Amplitude Modulated video camera - Light separation in dynam...

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

作者： Kolaman, Amir Lvov, Maxim Hagege, Rami Guterman, Hugo Electrical and Computer Engineering Department Ben-Gurion University of the Negev POB 653 Beer-Sheva8410501 Israel

ISBN: (纸本)9781467388511

Controlled light conditions improve considerably the performance of most computer vision algorithms. Dynamic light conditions create varying spatial changes in color and intensity across the scene. These condition, caused by a moving shadow for example, force developers to create algorithms which are robust to such variations. We suggest a computational camera which produces images that are not influenced by environmental variations in light conditions. The key insight is that many years ago, similar difficulties were already solved in radio communication; As a result each channel is immune to interference from other radio channels. Amplitude Modulated (AM) video camera separates the influence of a modulated light from other unknown light sources in the scene; Causing the AM video camera frame to appear the same - independent of the light conditions in which it was taken. We built a prototype of the AM video camera by using off the shelf hardware and tested it. AM video camera was used to demonstrate color constancy, shadow removal and contrast enhancement in real time. We show theoretically and empirically that:1. the proposed system can produce images with similar noise levels as a standard camera. 2. The images created by such camera are almost completely immune to temporal, spatial and spectral changes in the background light.

关键词： Video cameras

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Cascaded interactional targeting network for egocentric video analysis

Cascaded interactional targeting network for egocentric vide...

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

作者： Zhou, Yang Ni, Bingbing Hong, Richang Yang, Xiaokang Tian, Qi University of Texas San Antonio United States Shanghai Jiao Tong University China HeFei University of Technology China

ISBN: (纸本)9781467388511

Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly address these two issues via introducing a cascaded interactional targeting (i.e., infer both hand and active object regions) deep neural network. Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i.e., massive bounding box annotations) with a small set of strongly supervised data (i.e., fully annotated hand segmentation maps) to achieve stateof-the-art hand segmentation performance. Secondly, the resulting high-quality hand segmentation maps are further paired with the corresponding motion maps and object feature maps, in order to explore the contextual information among object, motion and hand to generate interactional foreground regions (operated objects). The resulting interactional target maps (hand + active object) from our cascaded DCNN are further utilized to form discriminative action representation. Experiments show that our framework has achieved the state-of-the-art egocentric action recognition performance on the benchmark dataset Activities of Daily Living (ADL).

关键词： pattern recognition

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NetVLAD: CNN architecture for weakly supervised place recognition

NetVLAD: CNN architecture for weakly supervised place recogn...

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

作者： Arandjelovi, Relja Gronat, Petr Torii, Akihiko Pajdla, Tomas Sivic, Josef WILLOW Project Departement d'Informatique de l'École Normale Supérieure ENS/INRIA/CNRS UMR 8548 France Department of Mechanical and Control Engineering Graduate School of Science and Engineering Tokyo Institute of Technology Japan Center for Machine Perception Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (纸本)9781467388511

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following three principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we develop a training procedure, based on a new weakly supervised ranking loss, to learn parameters of the architecture in an end-to-end manner from images depicting the same places over time downloaded from Google Street View Time Machine. Finally, we show that the proposed architecture significantly outperforms non-learnt image representations and off-the-shelf CNN descriptors on two challenging place recognition benchmarks, and improves over current stateofthe-art compact image representations on standard image retrieval benchmarks.

关键词： Image retrieval

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