pwc-archive/papers-with-abstracts huggingface dataset:
One Millisecond Face Alignment with an Ensemble of Regression Trees
Authors: Vahid Kazemi, Josephine Sullivan
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Data Augmentation, Face Alignment, feature selection, regression

This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.
Discriminative Deep Metric Learning for Face Verification in the Wild
Authors: Junlin Hu, Jiwen Lu, Yap-Peng Tan
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Face Verification, Metric Learning

This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild. Different from existing metric learning-based face verification methods which aim to learn a Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, simultaneously, the proposed DDML trains a deep neural network which learns a set of hierarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold, respectively, so that discriminative information can be exploited in the deep network. Our method achieves very competitive face verification performance on the widely used LFW and YouTube Faces (YTF) datasets.
Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses
Authors: Meina Kan, Shiguang Shan, Hong Chang, Xilin Chen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Face Recognition, Pose Estimation

Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity. Inspired by the observation that pose variations change non-linearly but smoothly, we propose to learn pose-robust features by modeling the complex non-linear transform from the non-frontal face images to frontal ones through a deep network in a progressive way, termed as stacked progressive auto-encoders (SPAE). Specifically, each shallow progressive auto-encoder of the stacked network is designed to map the face images at large poses to a virtual view at smaller ones, and meanwhile keep those images already at smaller poses unchanged. Then, stacking multiple these shallow auto-encoders can convert non-frontal face images to frontal ones progressively, which means the pose variations are narrowed down to zero step by step. As a result, the outputs of the topmost hidden layers of the stacked network contain very small pose variations, which can be used as the pose-robust features for face recognition. An additional attractiveness of the proposed method is that no pose estimation is needed for the test images. The proposed method is evaluated on two datasets with pose variations, i.e., MultiPIE and FERET datasets, and the experimental results demonstrate the superiority of our method to the existing works, especially to those 2D ones.
Deep Learning Face Representation from Predicting 10,000 Classes
Authors: Yi Sun, Xiaogang Wang, Xiaoou Tang
Date: 2014-06-01 00:00:00
Proceeding: Conference 2014 1
Tasks: Deep Learning, Face Identification, Face Verification

This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set. Moreover, the generalization capability of DeepID increases as more face classes are to be predicted at training. DeepID features are taken from the last hidden layer neuron activations of deep convolutional networks (ConvNets). When learned as classifiers to recognize about 10,000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons. The proposed features are extracted from various face regions to form complementary and over-complete representations. Any state-of-the-art classifiers can be learned based on these high-level representations for face verification. 97.45% verification accuracy on LFW is achieved with only weakly aligned faces.
3D-aided Face Recognition Robust to Expression and Pose Variations
Authors: Baptiste Chu, Sami Romdhani, Liming Chen
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Face Recognition

Expression and pose variations are major challenges for reliable face recognition (FR) in 2D. In this paper, we aim to endow state of the art face recognition SDKs with robustness to facial expression variations and pose changes by using an extended 3D Morphable Model (3DMM) which isolates identity variations from those due to facial expressions. Specifically, given a probe with expression, a novel view of the face is generated where the pose is rectified and the expression neutralized. We present two methods of expression neutralization. The first one uses prior knowledge to infer the neutral expression image from an input image. The second method, specifically designed for verification, is based on the transfer of the gallery face expression to the probe. Experiments using rectified and neutralized view with a standard commercial FR SDK on two 2D face databases, namely Multi-PIE and AR, show significant performance improvement of the commercial SDK to deal with expression and pose variations and demonstrates the effectiveness of the proposed approach.
Learning Non-Linear Reconstruction Models for Image Set Classification
Authors: Munawar Hayat, Mohammed Bennamoun, Senjian An
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, Face Recognition, General Classification, Unsupervised Pre-training

We propose a deep learning framework for image set classification with application to face recognition. An Adaptive Deep Network Template (ADNT) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The pre-initialized ADNT is then separately trained for images of each class and class-specific models are learnt. Based on the minimum reconstruction error from the learnt class-specific models, a majority voting strategy is used for classification. The proposed framework is extensively evaluated for the task of image set classification based face recognition on Honda/UCSD, CMU Mobo, YouTube Celebrities and a Kinect dataset. Our experimental results and comparisons with existing state-of-the-art methods show that the proposed method consistently achieves the best performance on all these datasets.
Gesture Recognition Portfolios for Personalization
Authors: Angela Yao, Luc van Gool, Pushmeet Kohli
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Gesture Recognition, Handwriting Recognition

Human gestures, similar to speech and handwriting, are often unique to the individual. Training a generic classifier applicable to everyone can be very difficult and as such, it has become a standard to use personalized classifiers in speech and handwriting recognition. In this paper, we address the problem of personalization in the context of gesture recognition, and propose a novel and extremely efficient way of doing personalization. Unlike conventional personalization methods which learn a single classifier that later gets adapted, our approach learns a set (portfolio) of classifiers during training, one of which is selected for each test subject based on the personalization data.  We formulate classifier personalization as a selection problem and propose several algorithms to compute the set of candidate classifiers.  Our experiments show that such an approach is much more efficient than adapting the classifier parameters but can still achieve comparable or better results.
Sign Spotting using Hierarchical Sequential Patterns with Temporal Intervals
Authors: Eng-Jon Ong, Oscar Koller, Nicolas Pugeault, Richard Bowden
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

This paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs. To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequential patterns that contain temporal intervals called {\em Sequential Interval Patterns} (SIP). We then propose a novel multi-class classifier that organises different sequential interval patterns in a hierarchical tree structure called a Hierarchical SIP Tree (HSP-Tree). This allows one to exploit any subsequence sharing that exists between different SIPs of different classes. Multiple trees are then combined together into a forest of HSP-Trees resulting in a strong classifier that can be used to spot signs. We then show how the HSP-Forest can be used to spot sequences of signs that occur in an input video. We have evaluated the method on both concatenated sequences of isolated signs and continuous sign sequences. We also show that the proposed method is superior in robustness and accuracy to a state of the art sign recogniser when applied to spotting a sequence of signs.
Automatic Feature Learning for Robust Shadow Detection
Authors: Salman Hameed Khan, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Shadow Detection

We present a practical framework to automatically detect shadows in real world scenes from a single photograph. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The 7-layer network architecture of each ConvNet consists of alternating convolution and sub-sampling layers. The proposed framework learns features at the super-pixel level and along the object boundaries. In both cases, features are extracted using a context aware window centered at interest points. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow contours. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.
Adaptive Object Retrieval with Kernel Reconstructive Hashing
Authors: Haichuan Yang, Xiao Bai, Jun Zhou, Peng Ren, Zhihong Zhang, Jian Cheng
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Retrieval, valid

Hashing is very useful for fast approximate similarity search on large database. In the unsupervised settings, most hashing methods aim at preserving the similarity defined by Euclidean distance. Hash codes generated by these approaches only keep their Hamming distance corresponding to the pairwise Euclidean distance, ignoring the local distribution of each data point. This objective does not hold for k-nearest neighbors search. In this paper, we firstly propose a new adaptive similarity measure which is consistent with k-NN search, and prove that it leads to a valid kernel. Then we propose a hashing scheme which uses binary codes to preserve the kernel function. Using low-rank approximation, our hashing framework is more effective than existing methods that preserve similarity over arbitrary kernel. The proposed kernel function, hashing framework, and their combination have demonstrated significant advantages compared with several state-of-the-art methods.
Detecting Objects using Deformation Dictionaries
Authors: Bharath Hariharan, C. L. Zitnick, Piotr Dollar
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object

Several popular and effective object detectors separately model intra-class variations arising from deformations and appearance changes. This reduces model complexity while enabling the detection of objects across changes in view- point, object pose, etc. The Deformable Part Model (DPM) is perhaps the most successful such model to date. A common assumption is that the exponential number of templates enabled by a DPM is critical to its success. In this paper, we show the counter-intuitive result that it is possible to achieve similar accuracy using a small dictionary of deformations. Each component in our model is represented by a single HOG template and a dictionary of flow fields that determine the deformations the template may undergo. While the number of candidate deformations is dramatically fewer than that for a DPM, the deformed templates tend to be plausible and interpretable. In addition, we discover that the set of deformation bases is actually transferable across object categories and that learning shared bases across similar categories can boost accuracy.
Persistence-based Structural Recognition
Authors: Chunyuan Li, Maks Ovsjanikov, Frederic Chazal
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Shape Classification, 3D Shape Retrieval, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Object Recognition, Retrieval, Texture Classification

This paper presents a framework for object recognition using topological persistence. In particular, we show that the so-called persistence diagrams built from functions defined on the objects can serve as compact and informative descriptors for images and shapes. Complementary to the bag-of-features representation, which captures the distribution of values of a given function, persistence diagrams can be used to characterize its structural properties, reflecting spatial information in an invariant way. In practice, the choice of function is simple: each dimension of the feature vector can be viewed as a function. The proposed method is general: it can work on various multimedia data, including 2D shapes, textures and triangle meshes. Extensive experiments on 3D shape retrieval, hand gesture recognition and texture classification demonstrate the performance of the proposed method in comparison with state-of-the-art methods. Additionally, our approach yields higher recognition accuracy when used in conjunction with the bag-of-features.
Inferring Unseen Views of People
Authors: Chao-Yeh Chen, Kristen Grauman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We pose unseen view synthesis as a probabilistic tensor completion problem. Given images of people organized by their rough viewpoint, we form a 3D appearance tensor indexed by images (pose examples), viewpoints, and image positions. After discovering the low-dimensional latent factors that approximate that tensor, we can impute its missing entries. In this way, we generate novel synthetic views of peopleeven when they are observed from just one camera viewpoint. We show that the inferred views are both visually and quantitatively accurate. Furthermore, we demonstrate their value for recognizing actions in unseen views and estimating viewpoint in novel images. While existing methods are often forced to choose between data that is either realistic or multi-view, our virtual views offer both, thereby allowing greater robustness to viewpoint in novel images.
Birdsnap: Large-scale Fine-grained Visual Categorization of Birds
Authors: Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L. Alexander, David W. Jacobs, Peter N. Belhumeur
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Fine-Grained Visual Categorization

We address the problem of large-scale fine-grained visual categorization, describing new methods we have used to produce an online field guide to 500 North American bird species.  We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds.  First, we introduce   "one-vs-most classifiers."  By eliminating highly similar species during training, these classifiers achieve more accurate and intuitive results than common one-vs-all classifiers.  Second, we show how to estimate spatio-temporal class priors from observations that are sampled at irregular and biased locations.  We show how these priors can be used to significantly improve performance.  We then show state-of-the-art recognition performance on a new, large dataset that we make publicly available.  These recognition methods are integrated into the online field guide, which is also publicly available.
Predicting Object Dynamics in Scenes
Authors: David F. Fouhey, C. L. Zitnick
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute, Object

Given a static scene, a human can trivially enumerate the myriad of things that can happen next and characterize the relative likelihood of each. In the process, we make use of enormous amounts of commonsense knowledge about how the world works. In this paper, we investigate learning this commonsense knowledge from data. To overcome a lack of densely annotated spatiotemporal data, we learn from sequences of abstract images gathered using crowdsourcing. The abstract scenes provide both object location and attribute information. We demonstrate qualitatively and quantitatively that our models produce plausible scene predictions on both the abstract images, as well as natural images taken from the Internet.
Enriching Visual Knowledge Bases via Object Discovery and Segmentation
Authors: Xinlei Chen, Abhinav Shrivastava, Abhinav Gupta
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object Discovery, Segmentation

There have been some recent efforts to build visual knowledge bases from Internet images. But most of these approaches have focused on bounding box representation of objects. In this paper, we propose to enrich these knowledge bases by automatically discovering objects and their segmentations from noisy Internet images. Specifically, our approach combines the power of generative modeling for segmentation with the effectiveness of discriminative models for detection. The key idea behind our approach is to learn and exploit top-down segmentation priors based on visual subcategories. The strong priors learned from these visual subcategories are then combined with discriminatively trained detectors and bottom up cues to produce clean object segmentations. Our experimental results indicate state-of-the-art performance on the difficult dataset introduced by Rubinstein et al. We have integrated our algorithm in NEIL for enriching its knowledge base. As of 14th April 2014, NEIL has automatically generated approximately 500K segmentations using web data.
Seeing the Arrow of Time
Authors: Lyndsey C. Pickup, Zheng Pan, Donglai Wei, YiChang Shih, Chang-Shui Zhang, Andrew Zisserman, Bernhard Scholkopf, William T. Freeman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification, Video Compression

We explore whether we can observe Time's Arrow in a temporal sequence--is it possible to tell whether a video is running forwards or backwards? We investigate this somewhat philosophical question using computer vision and machine learning techniques.  We explore three methods by which we might detect Time's Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time. We demonstrate good video forwards/backwards classification results on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and examine what motions the models have learned that help discriminate forwards from backwards time.
Hierarchical Feature Hashing for Fast Dimensionality Reduction
Authors: Bin Zhao, Eric P. Xing
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, Dimensionality Reduction, General Classification, Image Categorization, Multi-class Classification, Object Recognition, Scene Classification

Curse of dimensionality is a practical and challenging problem in image categorization, especially in cases with a large number of classes. Multi-class classification encounters severe computational and storage problems when dealing with these large scale tasks. In this paper, we propose hierarchical feature hashing to effectively reduce dimensionality of parameter space without sacrificing classification accuracy, and at the same time exploit information in semantic taxonomy among categories. We provide detailed theoretical analysis on our proposed hashing method. Moreover, experimental results on object recognition and scene classification further demonstrate the effectiveness of hierarchical feature hashing.
Modeling Image Patches with a Generic Dictionary of Mini-Epitomes
Authors: George Papandreou, Liang-Chieh Chen, Alan L. Yuille
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, image-classification, Image Classification

The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting. Key ingredient of the proposed model is a compact dictionary of mini-epitomes, learned in an unsupervised fashion on a large collection of images. The use of epitomes allows us to explicitly account for photometric and position variability in image appearance. We show that this flexibility considerably increases the capacity of the dictionary to accurately approximate the appearance of image patches and support recognition tasks. For image classification, we develop histogram-based image encoding methods tailored to the epitomic representation, as well as an "epitomic footprint" encoding which is easy to visualize and highlights the generative nature of our model. We discuss in detail computational aspects and develop efficient algorithms to make the model scalable to large tasks. The proposed techniques are evaluated with   experiments on the challenging PASCAL VOC 2007 image classification benchmark.