pwc-archive/papers-with-abstracts huggingface dataset:
Large Scale Hard Sample Mining With Monte Carlo Tree Search
Authors: Olivier Canevet, Francois Fleuret
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Detection, object-detection, Object Detection, Pedestrian Detection

We investigate an efficient strategy to collect false positives from very large training sets in the context of object detection. Our approach scales up the standard bootstrapping procedure by using a hierarchical decomposition of an image collection which reflects the statistical regularity of the detector's responses.  Based on that decomposition, our procedure uses a Monte Carlo Tree Search to prioritize the sampling toward sub-families of images which have been observed to be rich in false positives, while maintaining a fraction of the sampling toward unexplored sub-families of images. The resulting procedure increases substantially the proportion of false positive samples among the visited ones compared to a naive uniform sampling.  We apply experimentally this new procedure to face detection with a collection of 100,000 background images and to pedestrian detection with 32,000 images. We show that for two standard detectors, the proposed strategy cuts the number of images to visit by half to obtain the same amount of false positives and the same final performance.
Multi-Label Ranking From Positive and Unlabeled Data
Authors: Atsushi Kanehira, Tatsuya Harada
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this paper, we specifically examine the training of a multi-label classifier from data with incompletely assigned labels. This problem is fundamentally important in many multi-label applications because it is almost impossible for human annotators to assign a complete set of labels, although their judgments are reliable. In other words, a multi-label dataset usually has properties by which (1) assigned labels are definitely positive and (2) some labels are absent but are still considered positive. Such a setting has been studied as a positive and unlabeled (PU) classification problem in a binary setting. We treat incomplete label assignment problems as a multi-label PU ranking, which is an extension of classical binary PU problems to the well-studied rank-based multi-label classification. We derive the conditions that should be satisfied to cancel the negative effects of label incompleteness. Our experimentally obtained results demonstrate the effectiveness of these conditions.
Symmetry reCAPTCHA
Authors: Chris Funk, Yanxi Liu
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Symmetry Detection

This is a reaction to the poor performance of symmetry detection algorithms on real-world images, benchmarked since CVPR 2011. Our systematic study reveals significant difference between human labeled (reflection and rotation) symmetries on photos and the output of computer vision algorithms on the same photo set. We exploit this human-machine symmetry perception gap by proposing a novel symmetry-based Turing test. By leveraging a comprehensive user interface, we collected more than 78,000 symmetry labels from 400 Amazon Mechanical Turk raters on nearly 1,000 photos from the Microsoft COCO dataset. Using a set of ground-truth symmetries automatically generated from noisy human labels, the effectiveness of our work is evidenced by a separate test where over 96% success rate is achieved. We demonstrate statistically significant outcomes for using symmetry perception as a powerful, alternative, image-based reCAPTCHA.
Unsupervised Learning of Discriminative Attributes and Visual Representations
Authors: Chen Huang, Chen Change Loy, Xiaoou Tang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Attribute, Clustering, Contour Detection, Image Retrieval, object-detection, Object Detection, Retrieval

Attributes offer useful mid-level features to interpret visual data. While most attribute learning methods are supervised by costly human-generated labels, we introduce a simple yet powerful unsupervised approach to learn and predict visual attributes directly from data. Given a large unlabeled image collection as input, we train deep Convolutional Neural Networks (CNNs) to output a set of discriminative, binary attributes often with semantic meanings. Specifically, we first train a CNN coupled with unsupervised discriminative clustering, and then use the cluster membership as a soft supervision to discover shared attributes from the clusters while maximizing their separability. The learned attributes are shown to be capable of encoding rich imagery properties from both natural images and contour patches. The visual representations learned in this way are also transferrable to other tasks such as object detection. We show other convincing results on the related tasks of image retrieval and classification, and contour detection.
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification
Authors: Ha Quang Minh, Marco San Biagio, Loris Bazzani, Vittorio Murino
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification, image-classification, Image Classification, Object Recognition

This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features, in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds. Our formulation provides a rich representation of image features by exploiting their non-linear correlations, using the power of kernel methods and Riemannian geometry. Theoretically, we provide an approximate formulation for the Log-Hilbert-Schmidt distance between covariance operators that is efficient to compute and scalable to large datasets. Empirically, we apply our framework to the task of image classification on eight different, challenging datasets. In almost all cases, the results obtained outperform other state of the art methods, demonstrating the competitiveness and potential of our framework.
Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming
Authors: Yongfang Cheng, Yin Wang, Mario Sznaier, Octavia Camps
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Clustering

 This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem  can be formulated as a convex semi-definite optimization problem subject to an additional  rank constrain that involves only a very small number of variables.  This is established by first reducing the problem to a (generically non-convex) quadratically constrained quadratic problem and then using its special sparse structure to find conditions guaranteeing that a suitably built convex relaxation is indeed exact.  When combined with the commonly used nuclear norm relaxation for rank, the results above lead to computationally efficient algorithms with optimality guarantees. A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise,  co-ocurrences, and percentage of outliers. These results are illustrated  with several examples where the proposed algorithm is shown to outperform existing approaches.
Robust Tensor Factorization With Unknown Noise
Authors: Xi'ai Chen, Zhi Han, Yao Wang, Qian Zhao, Deyu Meng, Yandong Tang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

Because of the limitations of matrix factorization, such as losing spatial structure information, the concept of tensor factorization has been applied for the recovery of a low dimensional subspace from high dimensional visual data. Generally, the recovery is achieved by minimizing the loss function between the observed data and the factorization representation. Under different assumptions of the noise distribution, the loss functions are in various forms, like L1 and L2 norms. However, real data are often corrupted by noise with an unknown distribution. Then any specific form of loss function for one specific kind of noise often fails to tackle such real data with unknown noise. In this paper, we propose a tensor factorization algorithm to model the noise as a Mixture of Gaussians (MoG). As MoG has the ability of universally approximating any hybrids of continuous distributions, our algorithm can effectively recover the low dimensional subspace from various forms of noisy observations. The parameters of MoG are estimated under the EM framework and through a new developed algorithm of weighted low-rank tensor factorization (WLRTF). The effectiveness of our algorithm are substantiated by extensive experiments on both of synthetic data and real image data.
Kernel Approximation via Empirical Orthogonal Decomposition for Unsupervised Feature Learning
Authors: Yusuke Mukuta, Tatsuya Harada
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

Kernel approximation methods are important tools for various machine learning problems. There are two major methods used to approximate the kernel function: the Nystrom method and the random features method.   However, the Nystrom 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 Nystrom 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.
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix Completion
Authors: Xavier Alameda-Pineda, Elisa Ricci, Yan Yan, Nicu Sebe
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification, Matrix Completion, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Advanced computer vision and machine learning techniques tried to automatically categorize the emotions elicited by abstract paintings with limited success. Since the annotation of the emotional content is highly resource-consuming, datasets of abstract paintings are either constrained in size or partially annotated. Consequently, it is natural to address the targeted task within a transductive framework. Intuitively, the use of multi-label classification techniques is desirable so to synergically exploit the relations between multiple latent variables, such as emotional content, technique, author, etc. A very popular approach for transductive multi-label recognition under linear classification settings is matrix completion. In this study we introduce non-linear matrix completion (NLMC), thus extending classical linear matrix completion techniques to the non-linear case. Together with the theory grounding the model, we propose an efficient optimization solver. As shown by our extensive experimental validation on two publicly available datasets, NLMC outperforms state-of-the-art methods when recognizing emotions from abstract paintings.
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations
Authors: Xian Wei, Hao Shen, Martin Kleinsteuber
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Dimensionality Reduction, Disentanglement, Representation Learning

This paper presents an algorithm that allows to learn low dimensional representations of images in an unsupervised manner. The core idea is to combine two criteria that play important roles in unsupervised representation learning, namely sparsity and trace quotient. The former is known to be a convenient tool to identify underlying factors, and the latter is known as a disentanglement of underlying discriminative factors. In this work, we develop a generic cost function for learning jointly a sparsifying dictionary and a dimensionality reduction transformation. It leads to several counterparts of classic low dimensional representation methods, such as Principal Component Analysis, Local Linear Embedding, and Laplacian Eigenmap. Our proposed optimisation algorithm leverages the efficiency of geometric optimisation on Riemannian manifolds and a closed form solution to the elastic net problem.
Backtracking ScSPM Image Classifier for Weakly Supervised Top-Down Saliency
Authors: Hisham Cholakkal, Jubin Johnson, Deepu Rajan
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: object-detection, Object Detection

Top-down saliency models produce a probability map that peaks at target locations specified by a task/goal such as object detection. They are usually trained in a supervised setting involving annotations of objects. We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence of an object in an image. First, the probabilistic contribution of each image patch to the confidence of an ScSPM-based classifier produces a Reverse-ScSPM (R-ScSPM) saliency map. Neighborhood information is then incorporated through a contextual saliency map which is estimated using logistic regression learnt on patches having high R-ScSPM saliency. Both the saliency maps are combined to obtain the final saliency map. We evaluate the performance of the proposed weakly supervised top-down saliency and achieves comparable performance with fully supervised approaches. Experiments are carried out on 5 challenging datasets across 3 different applications.
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
Authors: Jun Xu, Tao Mei, Ting Yao, Yong Rui
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Captioning, Sentence, Video Description, Video Understanding

While there has been increasing interest in the task of describing video with natural language, current computer vision algorithms are still severely limited in terms of the variability and complexity of the videos and their associated language that they can recognize.  This is in part due to the simplicity of current benchmarks, which mostly focus on specific fine-grained domains with limited videos and simple descriptions. While researchers have provided several benchmark datasets for image captioning, we are not aware of any large-scale video description dataset with comprehensive categories yet diverse video content.  In this paper we present MSR-VTT (standing for "ABC-Video to Text") which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text.  This is achieved by collecting 257 popular queries from a commercial video search engine, with 118 videos for each query. In its current version, MSR-VTT provides 10K web video clips with 38.7 hours and 200K clip-sentence pairs in total, covering the most comprehensive categories and diverse visual content, and representing the largest dataset in terms of sentence and vocabulary. Each clip is annotated with about 20 natural sentences by 1,327 AMT workers.  We present a detailed analysis of MSR-VTT in comparison to a complete set of existing datasets, together with a summarization of different state-of-the-art video-to-text approaches.  We also provide an extensive evaluation of these approaches on this dataset, showing that the hybrid Recurrent Neural Network-based approach, which combines single-frame and motion representations with soft-attention pooling strategy, yields the best generalization capability on MSR-VTT.
Simultaneous Clustering and Model Selection for Tensor Affinities
Authors: Zhuwen Li, Shuoguang Yang, Loong-Fah Cheong, Kim-Chuan Toh
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Clustering, model, Model Selection, Stochastic Optimization

Estimating the number of clusters remains a difficult model selection problem. We consider this problem in the domain where the affinity relations involve groups of more than two nodes. Building on the previous formulation for the pairwise affinity case, we exploit the mathematical structures in the higher order case. We express the original minimal-rank and positive semi-definite (PSD) constraints in a form amenable for numerical implementation, as the original constraints are either intractable or even undefined in general in the higher order case. To scale to large problem sizes, we also propose an alternative formulation, so that it can be efficiently solved via stochastic optimization in an online fashion. We evaluate our algorithm with different applications to demonstrate its superiority, and show it can adapt to varying levels of unbalancedness of clusters.
Discriminatively Embedded K-Means for Multi-View Clustering
Authors: Jinglin Xu, Junwei Han, Feiping Nie
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Clustering

In real world applications, more and more data, for example, image/video data, are high dimensional and represented by multiple views which describe different perspectives of the data. Efficiently clustering such data is a challenge. To address this problem, this paper proposes a novel multi-view clustering method called Discriminatively Embedded K-Means (DEKM), which embeds the synchronous learning of multiple discriminative subspaces into multi-view K-Means clustering to construct a unified framework, and adaptively control the intercoordinations between these subspaces simultaneously. In this framework, we firstly design a weighted multi-view Linear Discriminant Analysis (LDA), and then develop an unsupervised optimization scheme to alternatively learn the common clustering indicator, multiple discriminative subspaces and weights for heterogeneous features with convergence. Comprehensive evaluations on three benchmark datasets and comparisons with several state-of-the-art multi-view clustering algorithms demonstrate the superiority of the proposed work.
Min Norm Point Algorithm for Higher Order MRF-MAP Inference
Authors: Ishant Shanu, Chetan Arora, Parag Singla
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

 Many tasks in computer vision and machine learning can be modelled as the inference problems in an MRF-MAP formulation and can be reduced to minimizing a submodular function. Using higher order clique potentials to model complex dependencies between pixels improves the performance but the current state of the art inference algorithms fail to scale for larger clique sizes. We adapt a well known Min Norm Point algorithm from mathematical optimization literature to exploit the sum of submodular structure found in the MRF-MAP formulation. Unlike some contemporary methods, we do not make any assumptions (other than submodularity) on the type of the clique potentials. Current state of the art inference algorithms for general submodular function takes many hours for problems with clique size 16, and fail to scale beyond. On the other hand, our algorithm is highly efficient and can perform optimal inference in few seconds even on clique size an order of magnitude larger. The proposed algorithm can even scale to clique sizes of many hundreds, unlocking the usage of really large size cliques for MRF-MAP inference problems in computer vision. We demonstrate the efficacy of our approach by experimenting on synthetic as well as real datasets.
Learning Deep Representation for Imbalanced Classification
Authors: Chen Huang, Yining Li, Chen Change Loy, Xiaoou Tang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, General Classification, imbalanced classification, Representation Learning

Data in vision domain often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary classification methods based on deep convolutional neural network (CNN) typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both inter-cluster and inter-class margins. This tighter constraint effectively reduces the class imbalance inherent in the local data neighborhood. We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple-header hinge loss. The representation learned by our approach, when combined with a simple k-nearest neighbor (kNN) algorithm, shows significant improvements over existing methods on both high- and low-level vision classification tasks that exhibit imbalanced class distribution.
Sparse Coding for Third-Order Super-Symmetric Tensor Descriptors With Application to Texture Recognition
Authors: Piotr Koniusz, Anoop Cherian
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Dictionary Learning

Super-symmetric tensors - a higher-order extension of scatter matrices - are becoming increasingly popular in machine learning and computer vision for modeling data statistics, co-occurrences, or even as visual descriptors. They were shown recently to outperform second-order approaches, however, the size of these tensors are exponential in the data dimensionality, which is a significant concern. In this paper, we study third-order super-symmetric tensor descriptors in the context of dictionary learning and sparse coding. For this purpose, we propose a novel non-linear third-order texture descriptor. Our goal is to approximate these tensors as sparse conic combinations of atoms from a learned dictionary. Apart from the significant benefits to tensor compression that this framework offers, our experiments demonstrate that the sparse coefficients produced by this scheme lead to better aggregation of high-dimensional data and showcase superior performance on two common computer vision tasks compared to the state of the art.
Random Features for Sparse Signal Classification
Authors: Jen-Hao Rick Chang, Aswin C. Sankaranarayanan, B. V. K. Vijaya Kumar
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, General Classification

Random features is an approach for kernel-based inference on large datasets. In this paper, we derive  performance guarantees for random features on signals, like images, that enjoy sparse representations and show that the number of random features required to achieve a desired approximation of the kernel similarity matrix can be significantly smaller for sparse signals. Based on this, we propose a scheme termed compressive random features that first obtains low-dimensional projections of a dataset and, subsequently, derives random features on the low-dimensional projections. This scheme provides significant improvements in signal dimensionality, computational time, and storage costs over traditional random features while enjoying similar theoretical  guarantees for achieving  inference performance. We support our claims by providing empirical results across many datasets.