mtybilly/Instruct2DS huggingface dataset:
Symmetry reCAPTCHA
Authors: Yanxi Liu Chris Funk
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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 Change Loy, Xiaoou Tang Chen Huang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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.
When VLAD Met Hilbert
Authors: Mathieu Salzmann, Fatih Porikli Mehrtash Harandi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In many challenging visual recognition tasks where training data is limited,  Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerful image/video representations that compete with or outperform state-of-the-art approaches. In this paper, we address two fundamental limitations of VLAD:  its requirement for the local descriptors to have vector form and its restriction to linear classifiers due to its high-dimensionality. To this end, we introduce a kernelized version of VLAD. This not only lets us inherently exploit more sophisticated classification schemes, but also enables us to efficiently aggregate non-vector descriptors (e.g., manifold-valued data) in the VLAD framework. Furthermore, we propose an approximate formulation that allows us to accelerate the coding process while still benefiting from the properties of kernel VLAD. Our experiments demonstrate the effectiveness of our approach at handling manifold-valued data, such as covariance descriptors, on several classification tasks.  Our results also evidence the benefits of our nonlinear VLAD descriptors against the linear ones in Euclidean space using several standard benchmark datasets.
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification
Authors: Marco San Biagio, Loris Bazzani, Vittorio Murino Ha Quang Minh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Yin Wang, Mario Sznaier, Octavia Camps Yongfang Cheng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Zhi Han, Yao Wang, Qian Zhao, Deyu Meng, Yandong Tang Xi'ai Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Tatsuya Harada Yusuke Mukuta
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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.
Active Learning for Delineation of Curvilinear Structures
Authors: Raphael Sznitman, Przemyslaw Glowacki, Pascal Fua Agata Mosinska-Domanska
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Many recent delineation techniques owe  much of their increased effectiveness to path classification  algorithms that  make it possible to distinguish  promising paths from others. The downside of this  development is that they  require annotated training data, which is tedious to produce.  In this paper,  we propose an Active Learning approach  that considerably speeds up  the annotation  process. Unlike  standard ones,  it takes  advantage of  the specificities of the delineation problem. It  operates on a graph and can reduce the training  set size  by up  to 80%  without compromising  the reconstruction quality.  We  will  show  that  our  approach outperforms  conventional  ones  on  various biomedical  and  natural  image  datasets,  thus  showing  that  it  is  broadly applicable.
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix Completion
Authors: Elisa Ricci, Yan Yan, Nicu Sebe Xavier Alameda-Pineda
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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.
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
Authors: Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, Shuicheng Yan Canyi Lu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper studies the Tensor Robust Principal Component (TRPCA) problem which extends the known Robust PCA to the tensor case. Our model is based on a new tensor Singular Value Decomposition (t-SVD)  and its induced tensor tubal rank and tensor nuclear norm. Consider that we have a 3-way tensor X in R^n*n*n_3 such that X=L_0+S_0, where  L_0 has low tubal rank and S_0 is sparse. Is that possible to recover both components? In this work, we prove that under certain suitable assumptions, we can recover both the low-rank and the sparse components exactly by simply solving a convex program  whose objective is a weighted combination of the tensor nuclear norm and the l1-norm, i.e.,  min L,E s.t. ||L||_*+lambda||E||_1 s.t. X=L+E. where lambda=1/sqrtmax(n_1,n_2)n_3. Interestingly, TRPCA involves RPCA as a special case when n_3=1 and thus it is a simple and elegant tensor extension of RPCA. Also numerical experiments verify our theory and the application for the image denoising demonstrates the effectiveness of our method.
Sliced Wasserstein Kernels for Probability Distributions
Authors: Yang Zou, Gustavo K. Rohde Soheil Kolouri
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as powerful discrepancy measures for probability distributions. The recent developments on alternative formulations of the optimal transport have allowed for faster solutions to the problem and have revamped their practical applications in machine learning. In this paper, we exploit the widely used kernel methods and provide a family of provably positive definite kernels based on the Sliced Wasserstein distance and demonstrate the benefits of these kernels in a variety of learning tasks. Our work provides a new perspective on the application of optimal transport flavored distances through kernel methods in machine learning tasks.
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations
Authors: Hao Shen, Martin Kleinsteuber Xian Wei
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Jubin Johnson, Deepu Rajan Hisham Cholakkal
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Tao Mei, Ting Yao, Yong Rui Jun Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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.
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
Authors: Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef Sivic Relja Arandjelovic
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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 state-of-the-art compact image representations on standard image retrieval benchmarks.
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Authors: Amir R. Zamir, Silvio Savarese, Ashutosh Saxena Ashesh Jain
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-level structure and can benefit from it. Spatio-temporal graphs are a popular  tool for imposing such high-level intuitions in the formulation of real world problems. In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks (RNNs). We develop a scalable method for casting an arbitrary spatio-temporal graph as a rich RNN mixture that is feedforward, fully differentiable, and jointly trainable. The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps. The evaluations of the proposed approach on a diverse set of problems, ranging from modeling human motion to object interactions, shows improvement over the state-of-the-art with a large margin. We expect this method to empower  new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks.
Learning to Select Pre-Trained Deep Representations With Bayesian Evidence Framework
Authors: Taewoong Jang, Bohyung Han, Seungjin Choi Yong-Deok Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The regularization parameters in LS-SVM is estimated automatically without grid search and cross-validation by maximizing evidence, which is a useful measure to select the best performing CNN out of multiple candidates for transfer learning; the evidence is optimized efficiently by employing Aitken's delta-squared process, which accelerates convergence of fixed point update. The proposed Bayesian evidence framework also provides a good solution to identify the best ensemble of heterogeneous CNNs through a greedy algorithm. Our Bayesian evidence framework for transfer learning is tested on 12 visual recognition datasets and illustrates the state-of-the-art performance consistently in terms of prediction accuracy and modeling efficiency.
Synthesized Classifiers for Zero-Shot Learning
Authors: Wei-Lun Chao, Boqing Gong, Fei Sha Soravit Changpinyo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Given semantic descriptions of object classes, zero-shot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided. We propose to tackle this problem from the perspective of manifold learning. Our main idea is to align the semantic space that is derived from external information to the model space that concerns itself with recognizing visual features. To this end, we introduce a set of "phantom" object classes whose coordinates live in both the semantic space and the model space. Serving as bases in a dictionary, they can be optimized from labeled data such that the synthesized real object classifiers achieve optimal discriminative performance. We demonstrate superior accuracy of our approach over the state of the art on four benchmark datasets for zero-shot learning, including the full ImageNet Fall 2011 dataset with more than 20,000 unseen classes.
Semi-Supervised Vocabulary-Informed Learning
Authors: Leonid Sigal Yanwei Fu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of semi-supervised vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot and open set recognition using a unified framework. Specifically, we propose a maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms, ensuring that labeled samples are projected closest to their correct prototypes, in the embedding space, than to others. We show that resulting model shows improvements in supervised, zero-shot, and large open set recognition, with up to 310K class vocabulary on AwA and ImageNet datasets.
Simultaneous Clustering and Model Selection for Tensor Affinities
Authors: Shuoguang Yang, Loong-Fah Cheong, Kim-Chuan Toh Zhuwen Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Junwei Han, Feiping Nie Jinglin Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Chetan Arora, Parag Singla Ishant Shanu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Yining Li, Chen Change Loy, Xiaoou Tang Chen Huang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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.
Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions
Authors: Gustavo Carneiro, Ian Reid Vijay Kumar B G
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Recent innovations in training deep convolutional neural network (ConvNet) models have motivated the design of new methods to automatically learn local image descriptors. The latest deep ConvNets proposed for this task consist of a siamese network that is trained by penalising misclassification of pairs of local image patches. Current results from machine learning show that replacing this siamese by a triplet network can improve the classification accuracy in several problems, but this has yet to be demonstrated for local image descriptor learning. Moreover, current siamese and triplet networks have been trained with stochastic gradient descent that computes the gradient from individual pairs or triplets of local image patches, which can make them prone to overfitting. In this paper, we first propose the use of triplet networks for the problem of local image descriptor learning. Furthermore, we also propose the use of a global loss that minimises the overall classification error of all patches present in the training set, which can improve the generalisation capability of the model. Using the UBC benchmark dataset for comparing local image descriptors, we show that the triplet network produces a more accurate embedding than the siamese network in terms of the UBC dataset errors. Moreover, we also demonstrate that a combination of the triplet and global losses produces the best embedding in the field, using this triplet network. Finally, we also show that the use of the central-surround siamese network trained with the global loss produces the best result of the field on the UBC dataset.
Sparse Coding for Third-Order Super-Symmetric Tensor Descriptors With Application to Texture Recognition
Authors: Anoop Cherian Piotr Koniusz
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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: Aswin C. Sankaranarayanan, B. V. K. Vijaya Kumar Jen-Hao Rick Chang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 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.
High-Quality Depth From Uncalibrated Small Motion Clip
Authors: Sunghoon Im, Jaesik Park, Hae-Gon Jeon, In So Kweon Hyowon Ha
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a novel approach that generates a high-quality depth map from a set of images captured with a small viewpoint variation, namely small motion clip. As opposed to prior methods that recover scene geometry and camera motions using pre-calibrated cameras, we introduce a self-calibrating bundle adjustment tailored for small motion. This allows our dense stereo algorithm to produce a high-quality depth map for the user without the need for camera calibration. In the dense matching, the distributions of intensity profiles are analyzed to leverage the benefit of having negligible intensity changes within the scene due to the minuscule variation in viewpoint. The depth maps obtained by the proposed framework show accurate and extremely fine structures that are unmatched by previous literature under the same small motion configuration.
Efficient 3D Room Shape Recovery From a Single Panorama
Authors: Hui Zhang Hao Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a method to recover the shape of a 3D room from a full-view indoor panorama. Our algorithm can automatically infer a 3D shape from a collection of partially oriented superpixel facets and line segments. The core part of the algorithm is a constraint graph, which includes lines and superpixels as vertices, and encodes their geometric relations as edges. A novel approach is proposed to perform 3D reconstruction based on the constraint graph by solving all the geometric constraints as constrained linear least-squares. The selected constraints used for reconstruction are identified using an occlusion detection method with a Markov random field. Experiments show that our method can recover room shapes that can not be addressed by previous approaches. Our method is also efficient, that is, the inference time for each panorama is less than 1 minute.
Structured Prediction of Unobserved Voxels From a Single Depth Image
Authors: Oisin Mac Aodha, Simon Julier, Gabriel J. Brostow Michael Firman
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Building a complete 3D model of a scene, given only a single depth image, is underconstrained. To gain a full volumetric model, one needs either multiple views, or a single view together with a library of unambiguous 3D models that will fit the shape of each individual object in the scene.  We hypothesize that objects of dissimilar semantic classes often share similar 3D shape components, enabling a limited dataset to model the shape of a wide range of objects, and hence estimate their hidden geometry. Exploring this hypothesis, we propose an algorithm that can complete the unobserved geometry of tabletop-sized objects, based on a supervised model trained on already available volumetric elements. Our model maps from a local observation in a single depth image to an estimate of the surface shape in the surrounding neighborhood. We validate our approach both qualitatively and quantitatively on a range of indoor object collections and challenging real scenes.
HyperDepth: Learning Depth From Structured Light Without Matching
Authors: Christoph Rhemann, Vladimir Tankovich, Adarsh Kowdle, Sergio Orts Escolano, David Kim, Shahram Izadi Sean Ryan Fanello
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Structured light sensors are popular due to their robustness to untextured scenes and multipath. These systems triangulate depth by solving a correspondence problem between each camera and projector pixel. This is often framed as a local stereo matching task, correlating patches of pixels in the observed and reference image. However, this is computationally intensive, leading to reduced depth accuracy and framerate. We contribute an algorithm for solving this correspondence problem efficiently, without compromising depth accuracy. For the first time, this problem is cast as a classification-regression task, which we solve extremely efficiently using an ensemble of cascaded random forests. Our algorithm scales in number of disparities, and each pixel can be processed independently, and in parallel. No matching or even access to the corresponding reference pattern is required at runtime, and regressed labels are directly mapped to depth. Our GPU-based algorithm runs at a 1KHz for 1.3MP input/output images, with disparity error of 0.1 subpixels. We show a prototype high framerate depth camera running at 375Hz, useful for solving tracking-related problems. We demonstrate our algorithmic performance, creating high resolution real-time depth maps that surpass the quality of current state of the art depth technologies, highlighting quantization-free results with reduced holes, edge fattening and other stereo-based depth artifacts.
SVBRDF-Invariant Shape and Reflectance Estimation From Light-Field Cameras
Authors: Manmohan Chandraker, Alexei A. Efros, Ravi Ramamoorthi Ting-Chun Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture. However, obtaining the shape of glossy objects like metals, plastics or ceramics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In this paper, we derive a spatially-varying (SV)BRDF-invariant theory for recovering 3D shape and reflectance from light-field cameras. Our key theoretical insight is a novel analysis of diffuse plus single-lobe SVBRDFs under a light-field setup. We show that, although direct shape recovery is not possible, an equation relating depths and normals can still be derived. Using this equation, we then propose using a polynomial (quadratic) shape prior to resolve the shape ambiguity. Once shape is estimated, we can also recover the reflectance. We present extensive synthetic data on the entire MERL BRDF dataset, as well as a number of real examples to validate the theory, where we simultaneously recover shape and BRDFs from a single image taken with a Lytro Illum camera.
Semantic 3D Reconstruction With Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint
Authors: Christian Hane, Lubor Ladicky, Marc Pollefeys Nikolay Savinov
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose an approach for dense semantic 3D reconstruction which uses a data term that is defined as potentials over viewing rays, combined with continuous surface area penalization. Our formulation is a convex relaxation which we augment with a crucial non-convex constraint that ensures exact handling of visibility. To tackle the non-convex minimization problem, we propose a majorize-minimize type strategy which converges to a critical point. We demonstrate the benefits of using the non-convex constraint experimentally. For the geometry-only case, we set a new state of the art on two datasets of the commonly used Middlebury multi-view stereo benchmark. Moreover, our general-purpose formulation directly reconstructs thin objects, which are usually treated with specialized algorithms. A qualitative evaluation on the dense semantic 3D reconstruction task shows that we improve significantly over previous methods.
Theory and Practice of Structure-From-Motion Using Affine Correspondences
Authors: Joao P. Barreto Carolina Raposo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Affine Correspondences (ACs) are more informative than Point Correspondences (PCs) that are used as input in mainstream algorithms for Structure-from-Motion (SfM). Since ACs enable to estimate models from fewer correspondences, its use can dramatically reduce the number of combinations during the iterative step of sample-and-test that exists in most SfM pipelines. However, using ACs instead of PCs as input for SfM passes by fully understanding the relations between ACs and multi-view geometry, as well as by establishing practical, effective AC-based algorithms. This article is a step forward into this direction, by providing a clear account about how ACs constrain the two-view geometry, and by proposing new algorithms for plane segmentation and visual odometry that compare favourably with respect to methods relying in PCs.
Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction
Authors: Konrad Schindler Silvano Galliani
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We present a multi-view reconstruction method that combines conventional multi-view stereo (MVS) with appearance-based normal prediction, to obtain dense and accurate 3D surface models. Reliable surface normals reconstructed from multi-view correspondence serve as training data for a convolutional neural network (CNN), which predicts continuous normal vectors from raw image patches. By training from known points in the same image,  the prediction is specifically tailored to the materials and lighting conditions of the particular scene, as well as to the precise camera viewpoint. It is therefore a lot easier to learn than generic single-view normal estimation. The estimated normal maps, together with the known depth values  from MVS, are integrated to dense depth maps, which in turn are fused into a 3D model. Experiments on the DTU dataset show that our method delivers 3D reconstructions  with the same accuracy as MVS, but with significantly higher completeness.
From Dusk Till Dawn: Modeling in the Dark
Authors: Johannes L. Schonberger, Dinghuang Ji, Jan-Michael Frahm, Ondrej Chum, Jiri Matas Filip Radenovic
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Internet photo collections naturally contain a large variety of illumination conditions, with the largest difference between day and night images. Current modeling techniques do not embrace the broad illumination range often leading to reconstruction failure or severe artifacts. We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information. The proposed method relies on automatic scene appearance grouping, which is used to obtain separate dense 3D models. Subsequent model fusion combines the separate models into a complete and accurate reconstruction of the scene. In addition, we propose a method to derive the appearance information for the model under the different illumination conditions, even for scene parts that are not observed under one illumination condition. To achieve this, we develop a cross-illumination color transfer technique. We evaluate our method on a large variety of landmarks from across Europe reconstructed from a database of 7.4M images.
Accelerated Generative Models for 3D Point Cloud Data
Authors: Kihwan Kim, Alejandro Troccoli, Alonzo Kelly, Jan Kautz Benjamin Eckart
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Finding meaningful, structured representations of 3D point cloud data (PCD) has become a core task for spatial perception applications.    In this paper we introduce a method for constructing compact generative representations of PCD at multiple levels of detail.   As opposed to deterministic structures such as voxel grids or octrees, we propose probabilistic subdivisions of the data through local mixture modeling, and show how these subdivisions can provide a maximum likelihood segmentation of the data. The final representation is hierarchical, compact, parametric, and statistically derived, facilitating run-time occupancy calculations through stochastic sampling. Unlike traditional deterministic spatial subdivision methods, our technique enables dynamic creation of voxel grids according the application's best needs.   In contrast to other generative models for PCD, we explicitly enforce sparsity among points and mixtures, a technique which we call expectation sparsification. This leads to a highly parallel hierarchical Expectation Maximization (EM) algorithm well-suited for the GPU and real-time execution. We explore the trade-offs between model fidelity and model size at various levels of detail, our tests showing favorable performance when compared to octree and NDT-based methods.