pwc-archive/papers-with-abstracts huggingface dataset:
Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification
Authors: Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: General Classification, Stochastic Optimization

Learning the representation of shape cues in 2D & 3D objects for recognition is a fundamental task in computer vision. Deep neural networks (DNNs) have shown promising performance on this task.  Due to the large variability of shapes,  accurate recognition relies on good estimates of model uncertainty, ignored in traditional training of DNNs, typically learned via  stochastic optimization. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (SG-MCMC) to learn weight uncertainty in DNNs. It yields principled Bayesian interpretations for the commonly used Dropout/DropConnect techniques and incorporates them into the SG-MCMC framework. Extensive experiments on 2D & 3D shape datasets and various DNN models demonstrate the superiority of the proposed approach over stochastic optimization. Our approach yields higher recognition accuracy when used in conjunction with Dropout and Batch-Normalization.
A Field Model for Repairing 3D Shapes
Authors: Duc Thanh Nguyen, Binh-Son Hua, Khoi Tran, Quang-Hieu Pham, Sai-Kit Yeung
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: model

This paper proposes a field model for repairing 3D shapes constructed from multi-view RGB data. Specifically, we represent a 3D shape in a Markov random field (MRF) in which the geometric information is encoded by random binary variables and the appearance information is retrieved from a set of RGB images captured at multiple viewpoints. The local priors in the MRF model capture the local structures of object shapes and are learnt from 3D shape templates using a convolutional deep belief network. Repairing a 3D shape is formulated as the maximum a posteriori (MAP) estimation in the corresponding MRF. Variational mean field approximation technique is adopted for the MAP estimation. The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes. Experimental results have shown the robustness and efficiency of the proposed method in repairing noisy and incomplete 3D shapes.
Efficient Deep Learning for Stereo Matching
Authors: Wenjie Luo, Alexander G. Schwing, Raquel Urtasun
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Deep Learning, General Classification, GPU, Multi-class Classification, Stereo Matching, Stereo Matching Hand

In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation. However, current architectures rely on siamese networks which exploit concatenation followed by further processing layers, requiring a minute of GPU computation per image pair. In contrast, in this paper we propose a matching network which is able to produce very accurate results in less than a second of GPU computation. Towards this goal, we exploit a product layer which simply computes the inner product between the two representations of a siamese architecture. We train our network by treating the problem as multi-class classification, where the classes are all possible disparities. This allows us to get calibrated scores, which result in much better matching performance when compared to existing approaches.
Efficient Coarse-To-Fine PatchMatch for Large Displacement Optical Flow
Authors: Yinlin Hu, Rui Song, Yunsong Li
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Optical Flow Estimation

As a key component in many computer vision systems, optical flow estimation, especially with large displacements, remains an open problem. In this paper we present a simple but powerful matching method works in a coarse-to-fine scheme for optical flow estimation. Inspired by the nearest neighbor field (NNF) algorithms, our approach, called CPM (Coarse-to-fine PatchMatch), blends an efficient random search strategy with the coarse-to-fine scheme for optical flow problem. Unlike existing NNF techniques, which is efficient but the results is often too noisy for optical flow caused by the lack of global regularization, we propose a propagation step with constrained random search radius between adjacent levels on the hierarchical architecture. The resulting correspondences enjoys a built-in smoothing effect, which is more suited for optical flow estimation than NNF techniques. Furthermore, our approach can also capture the tiny structures with large motions which is a problem for traditional coarse-to-fine optical flow algorithms. Interpolated by an edge-preserving interpolation method (EpicFlow), our method outperforms the state of the art on MPI-Sintel and KITTI, and runs much faster than the competing methods.
FANNG: Fast Approximate Nearest Neighbour Graphs
Authors: Ben Harwood, Tom Drummond
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: GPU

We present a new method for approximate nearest neighbour search on large datasets of high dimensional feature vectors, such as SIFT or GIST descriptors.  Our approach constructs a directed graph that can be efficiently explored for nearest neighbour queries. Each vertex in this graph represents a feature vector from the dataset being searched.  The directed edges are computed by exploiting the fact that, for these datasets, the intrinsic dimensionality of the local manifold-like structure formed by the elements of the dataset is significantly lower than the embedding space.  We also provide an efficient search algorithm that uses this graph to rapidly find the nearest neighbour to a query with high probability.   We show how the method can be adapted to give a strong guarantee of 100% recall where the query is within a threshold distance of its nearest neighbour. We demonstrate that our method is significantly more efficient than existing state of the art methods.  In particular, our GPU implementation can deliver 90% recall for queries on a data set of 1 million SIFT descriptors at a rate of over 1.2 million queries per second on a Titan X. Finally we also demonstrate how our method scales to datasets of 5M and 20M entries.
Exemplar-Driven Top-Down Saliency Detection via Deep Association
Authors: Shengfeng He, Rynson W. H. Lau, Qingxiong Yang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, Saliency Detection

Top-down saliency detection is a knowledge-driven search task. While some previous methods aim to learn this "knowledge" from category-specific data, others transfer existing annotations in a large dataset through appearance matching. In contrast, we propose in this paper a locate-by-exemplar strategy. This approach is challenging, as we only use a few exemplars (up to 4) and the appearances among the query object and the exemplars can be very different. To address it, we design a two-stage deep model to learn the intra-class association between the exemplars and query objects. The first stage is for learning object-to-object association, and the second stage is to learn background discrimination. Extensive experimental evaluations show that the proposed method outperforms different baselines and the category-specific models. In addition, we explore the influence of exemplar properties, in terms of exemplar number and quality. Furthermore, we show that the learned model is a universal model and offers great generalization to unseen objects.
Unconstrained Salient Object Detection via Proposal Subset Optimization
Authors: Jianming Zhang, Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

We aim at detecting salient objects in unconstrained images. In unconstrained images, the number of salient objects (if any) varies from image to image, and is not given. We present a salient object detection system that directly outputs a compact set of detection windows, if any, for an input image. Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects. Location proposals tend to be highly overlapping and noisy. Based on the Maximum a Posteriori principle, we propose a novel subset optimization framework to generate a compact set of detection windows out of noisy proposals. In experiments, we show that our subset optimization formulation greatly enhances the performance of our system, and our system attains 16-34% relative improvement in Average Precision compared with the state-of-the-art on three challenging salient object datasets.
A Paradigm for Building Generalized Models of Human Image Perception Through Data Fusion
Authors: Shaojing Fan, Tian-Tsong Ng, Bryan L. Koenig, Ming Jiang, Qi Zhao
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Imputation, Video Summarization

In many sub-fields, researchers collect datasets of human ground truth that are used to create a new algorithm. For example, in research on image perception, datasets have been collected for topics such as what makes an image aesthetic or memorable. Despite high costs for human data collection, datasets are infrequently reused beyond their own fields of interest. Moreover, the algorithms built from them are domain-specific (predict a small set of attributes) and usually unconnected to one another. In this paper, we present a paradigm for building generalized and expandable models of human image perception. First, we fuse multiple fragmented and partially-overlapping datasets through data imputation. We then create a theoretically-structured statistical model of human image perception that is fit to the fused datasets. The resulting model has many advantages. (1) It is generalized, going beyond the content of the constituent datasets, and can be easily expanded by fusing additional datasets. (2) It provides a new ontology usable as a network to expand human data in a cost-effective way. (3) It can guide the design of a generalized computational algorithm for multi-dimensional visual perception. Indeed, experimental results show that a model-based algorithm outperforms state-of-the-art methods on predicting visual sentiment, visual realism and interestingness. Our paradigm can be used in various visual tasks (e.g., video summarization).
Saliency Unified: A Deep Architecture for Simultaneous Eye Fixation Prediction and Salient Object Segmentation
Authors: Srinivas S. S. Kruthiventi, Vennela Gudisa, Jaley H. Dholakiya, R. Venkatesh Babu
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, Semantic Segmentation

Human eye fixations often correlate with locations of salient objects in the scene. However, only a handful of approaches have attempted to simultaneously address the related aspects of eye fixations and object saliency. In this work, we propose a deep convolutional neural network (CNN) capable of predicting eye fixations and segmenting salient objects in a unified framework. We design the initial network layers, shared between both the tasks, such that they capture the object level semantics and the global contextual aspects of saliency, while the deeper layers of the network address task specific aspects. In addition, our network captures saliency at multiple scales via inception-style convolution blocks. Our network shows a significant improvement over the current state-of-the-art for both eye fixation prediction and salient object segmentation across a number of challenging datasets.
Estimating Correspondences of Deformable Objects "In-The-Wild"
Authors: Yuxiang Zhou, Epameinondas Antonakos, Joan Alabort-i-Medina, Anastasios Roussos, Stefanos Zafeiriou
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Semantic Segmentation

During the past few years we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models (SDMs). The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large amount of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. human body, it is difficult to define a consistent set of landmarks, and, thus, it becomes impossible to train humans in order to accurately annotate a collection of images. Nevertheless, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing. In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, we show that, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We employ our framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for body joints.
Gravitational Approach for Point Set Registration
Authors: Vladislav Golyanik, Sk Aziz Ali, Didier Stricker
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

In this paper a new astrodynamics inspired rigid point set registration algorithm is introduced -- the Gravitational Approach (GA). We formulate point set registration as a modified N-body problem with additional constraints and obtain an algorithm with unique properties which is fully scalable with the number of processing cores. In GA, a template point set moves in a viscous medium under gravitational forces induced by a reference point set. Pose updates are completed by numerically solving the differential equations of Newtonian mechanics. We discuss techniques for efficient implementation of the new algorithm and evaluate it on several synthetic and real-world scenarios. GA is compared with the widely used Iterative Closest Point and the state of the art rigid Coherent Point Drift algorithms. Experiments evidence that the new approach is robust against noise and can handle challenging scenarios with structured outliers.
Context-Aware Gaussian Fields for Non-Rigid Point Set Registration
Authors: Gang Wang, Zhicheng Wang, Yufei Chen, Qiangqiang Zhou, Weidong Zhao
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

Point set registration (PSR) is a fundamental problem in computer vision and pattern recognition, and it has been successfully applied to many applications. Although widely used, existing PSR methods cannot align point sets robustly under degradations, such as deformation, noise, occlusion, outlier, rotation, and multi-view changes. This paper proposes context-aware Gaussian fields (CA-LapGF) for non-rigid PSR subject to global rigid and local non-rigid geometric constraints, where a laplacian regularized term is added to preserve the intrinsic geometry of the transformed set. CA-LapGF uses a robust objective function and the quasi-Newton algorithm to estimate the likely correspondences, and the non-rigid transformation parameters between two point sets iteratively. The CA-LapGF can estimate non-rigid transformations, which are mapped to reproducing kernel Hilbert spaces, accurately and robustly in the presence of degradations. Experimental results on synthetic and real images reveal that how CA-LapGF outperforms state-of-the-art algorithms for non-rigid PSR.
Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC
Authors: Magnus Oskarsson, Kenneth Batstone, Kalle Astrom
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

In this paper we present a system for performing low rank matrix factorization. Low-rank matrix factorization is an essential problem in many areas including computer vision, with applications in e.g. affine structure-from-motion, photometric stereo, and non-rigid structure from motion. We specifically target structured data patterns, with outliers and large amounts of missing data. Using recently developed characterizations of minimal solutions to matrix factorization problems with missing data, we show how these can be used as building blocks in a hierarchical system that performs bootstrapping on all levels. This gives an robust and fast system, with state-of-the-art performance.
Relaxation-Based Preprocessing Techniques for Markov Random Field Inference
Authors: Chen Wang, Ramin Zabih
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

Markov Random Fields (MRFs) are a widely used graphical model, but the inference problem is NP-hard. For first-order MRFs with binary labels, Dead End Elimination (DEE) and QPBO can find the optimal labeling for some variables; the much harder case of larger label sets has been addressed by Kovtun and related methods which impose substantial computational overhead. We describe an efficient algorithm to correctly label a subset of the variables for arbitrary MRFs, with particularly good performance on binary MRFs. We propose a sufficient condition to check if a partial labeling is optimal, which is a generalization of DEE's purely local test. We give a hierarchy of relaxations that provide larger optimal partial labelings at the cost of additional computation. Empirical studies were conducted on several benchmarks, using expansion moves for inference. Our algorithm runs in a few seconds, and improves the speed of MRF inference with expansion moves by a factor of 1.5 to 12.
Sparse Coding for Classification via Discrimination Ensemble
Authors: Yuhui Quan, Yong Xu, Yuping Sun, Yan Huang, Hui Ji
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Classification, Dictionary Learning, General Classification

Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes.  Many existing approaches consider only a simple single linear classifier whose discriminative power is rather weak. In this paper, we proposed a discriminative sparse coding method which jointly learns a dictionary for sparse coding and an ensemble classifier for discrimination. The ensemble classifier is composed of a set of linear predictors and constructed via both subsampling on data and subspace projection on sparse codes. The advantages of the proposed method over the existing ones are multi-fold: better discriminability of sparse codes, weaker dependence on peculiarities of training data, and more expressibility of classifier for classification. These advantages are also justified in the experiments, as our method outperformed several state-of-the-art methods in several recognition tasks.
Guaranteed Outlier Removal With Mixed Integer Linear Programs
Authors: Tat-Jun Chin, Yang Heng Kee, Anders Eriksson, Frank Neumann
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: global-optimization

The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the problem size. Although randomized algorithms are much more efficient, the optimality of the solution is not guaranteed. Towards the goal of solving maximum consensus exactly, we present guaranteed outlier removal as a technique to reduce the runtime of exact algorithms. Specifically, before conducting global optimization, we attempt to remove data that are provably true outliers, i.e., those that do not exist in the maximum consensus set. We propose an algorithm based on mixed integer linear programming to perform the removal. The result of our algorithm is a smaller data instance that admits much faster solution by a subsequent exact algorithm, while yielding the same globally optimal result as the original problem. We demonstrate that overall speedups of up to 80% can be achieved on common vision problems.
Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization
Authors: Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, Qinfeng Shi
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: BIG-bench Machine Learning, Clustering, Matrix Completion

Trace-norm regularization plays an important role in many areas such as machine learning and computer vision. Solving trace-norm regularized Trace-norm regularization plays an important role in many areas such as computer vision and machine learning. When solving general large-scale trace-norm regularized problems, existing methods may be computationally expensive due to many high-dimensional truncated singular value decompositions (SVDs) or the unawareness of matrix ranks. In this paper, we propose a proximal Riemannian pursuit (PRP) paradigm which addresses a sequence of trace-norm regularized subproblems defined on nonlinear matrix varieties. To address the subproblem, we extend the proximal gradient method on vector space to nonlinear matrix varieties, in which the SVDs of intermediate solutions are maintained by cheap low-rank QR decompositions, therefore making the proposed method more scalable. Empirical studies on several tasks, such as matrix completion and low-rank representation based subspace clustering, demonstrate the competitive performance of the proposed paradigms over existing methods.
Minimizing the Maximal Rank
Authors: Erik Bylow, Carl Olsson, Fredrik Kahl, Mikael Nilsson
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Denoising

In computer vision, many problems can be formulated as finding a low rank approximation of a given measurement matrix. Ideally, if all elements of the measurement matrix are available, this is easily solved in the L2-norm using factorization. However, in practice this is rarely the case. Lately, this problem has been addressed using different approaches, one is to replace the rank term by the convex nuclear norm, another is to derive the convex envelope of the rank term plus a data term. In the latter case, matrices are divided into sub-matrices and the envelope is computed for each sub-block individually. In this paper a new convex envelope is derived which takes all sub-matrices into account simultaneously. This leads to a simpler formulation, using only one parameter, for applications where one seeks low rank approximations of multiple matrices with the same rank. We show in this paper how our general framework can be used for manifold denoising of several images at once, as well as just denoising one image. We get comparable results to other well-known methods and our framework can also be used for other applications such as linear shape models.
Solving Temporal Puzzles
Authors: Caglayan Dicle, Burak Yilmaz, Octavia Camps, Mario Sznaier
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Time Series, Time Series Analysis

Many physical phenomena, within short time windows, can be explained by low order differential relations. In a discrete world, these relations can be described using low order difference equations or equivalently low order auto regressive (AR) models. In this paper, based on this intuition, we propose an algorithm for solving time-sort temporal puzzles, defined as scrambled time series that need to be sorted out. We frame this highly combinatorial problem using a mixed-integer semi definite programming formulation and show how to turn it into a mixed-integer linear programming problem by using the recently introduced atomic norm framework. Our experiments show the effectiveness and generality of our approach in different scenarios.