pwc-archive/papers-with-abstracts huggingface dataset:
Continuous Visibility Feature
Authors: Guilin Liu, Yotam Gingold, Jyh-Ming Lien
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Segmentation

In this work, we propose a new type of visibility measurement named Continuous Visibility Feature (CVF). We say that a point q on the mesh is continuously visible from another point p if there exists a geodesic path connecting p and q that is entirely visible by p. In order to efficiently estimate the continuous visibility for all the vertices in a model, we propose two approaches that use specific CVF properties to avoid exhaustive visibility tests. CVF is then measured as the area of the continuously visible region. With this stronger visibility measure, we show that CVF better encodes the surface and part information of mesh than the tradition line-of-sight based visibility. For example, we show that existing segmentation algorithms can generate better segmentation results using CVF and its variants than using other visibility-based shape descriptors, such as shape diameter function. Similar to visibility and other mesh surface features, continuous visibility would have many applications.
FlowWeb: Joint Image Set Alignment by Weaving Consistent, Pixel-Wise Correspondences
Authors: Tinghui Zhou, Yong Jae Lee, Stella X. Yu, Alyosha A. Efros
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Optical Flow Estimation

Given a set of poorly aligned images of the same visual concept without any annotations, we propose an algorithm to jointly bring them into pixel-wise correspondence by estimating a FlowWeb representation of the image set. FlowWeb is a fully-connected correspondence flow graph with each node representing an image, and each edge representing the correspondence flow field between a pair of images, i.e. a vector field indicating how each pixel in one image can find a corresponding pixel in the other image. Correspondence flow is related to optical flow but allows for correspondences between visually dissimilar regions if there is evidence they correspond transitively on the graph. Our algorithm starts by initializing all edges of this complete graph with an off-the-shelf, pairwise flow method. We then iteratively update the graph to force it to be more self- consistent. Once the algorithm converges, dense, globally-consistent correspondences can be read off the graph. Our results suggest that FlowWeb improves alignment accuracy over previous pairwise as well as joint alignment methods.
Supervised Descriptor Learning for Multi-Output Regression
Authors: Xiantong Zhen, Zhijie Wang, Mengyang Yu, Shuo Li
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Head Pose Estimation, Pose Estimation, regression

Descriptor learning has recently drawn increasing attention in computer vision, Existing algorithms are mainly developed for classification rather than for regression which however has recently emerged as a powerful tool to solve a broad range of problems, e.g., head pose estimation. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm to establish a discriminative and compact feature representation for multi-output regression. By formulating as generalized low-rank approximations of matrices with a supervised manifold regularization (SMR), the SDL removes irrelevant and redundant information from raw features by transforming into a low-dimensional space under the supervision of multivariate targets. The obtained discriminative while compact descriptor largely reduces the variability and ambiguity in multi-output regression, and therefore enables more accurate and efficient multivariate estimation. We demonstrate the effectiveness of the proposed SDL algorithm on a representative multi-output regression task: head pose estimation using the benchmark Pointing'04 dataset. Experimental results show that the SDL can achieve high pose estimation accuracy and significantly outperforms state-of-the-art algorithms by an error reduction up to 27.5%. The proposed SDL algorithm provides a general descriptor learning framework in a supervised way for multi-output regression which can largely boost the performance of existing multi-output regression tasks.
A Statistical Model of Riemannian Metric Variation for Deformable Shape Analysis
Authors: Andrea Gasparetto, Andrea Torsello
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Retrieval

The analysis of deformable 3D shape is often cast in terms of the shape's intrinsic geometry due to its invariance to a wide range of non-rigid deformations. However,  object's plasticity in non-rigid transformation often results in transformations that are not completely isometric in the surface's geometry and whose mode of deviation from isometry is an identifiable characteristic of the shape and its deformation modes. In this paper, we propose a novel generative model of the variations of the intrinsic metric of deformable shapes, based on the spectral decomposition of the Laplace-Beltrami operator. To this end, we assume two independent models for the eigenvectors and  the eigenvalues of the graph-Laplacian of a 3d mesh which are learned in a supervised way from a set of shapes belonging to the same class. We show how this model can be efficiently learned given a set of 3D meshes, and evaluate the performance of the resulting generative model in shape classification and retrieval tasks. Comparison with state-of-the-art solutions for these problems confirm the validity of the approach.
Temporally Coherent Interpretations for Long Videos Using Pattern Theory
Authors: Fillipe Souza, Sudeep Sarkar, Anuj Srivastava, Jingyong Su
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Graph-theoretical methods have successfully provided semantic and structural interpretations of images and videos. A recent paper introduced a pattern-theoretic approach that allows construction of flexible graphs for representing interactions of actors with objects and inference is accomplished by an efficient annealing algorithm. Actions and objects are termed generators and their interactions are termed bonds; together they form high-probability configurations, or interpretations, of observed scenes. This work and other structural methods have generally been limited to analyzing short videos involving isolated actions. Here we provide an extension that uses additional temporal bonds across individual actions to enable semantic interpretations of longer videos. Longer temporal connections improve scene interpretations as they help discard (temporally) local solutions in favor of globally superior ones. Using this extension, we demonstrate improvements in understanding longer videos, compared to individual interpretations of non-overlapping time segments. We verified the success of our approach by generating interpretations for more than 700 video segments from the YouCook data set, with intricate videos that exhibit cluttered background, scenarios of occlusion,  viewpoint variations and changing conditions of illumination. Interpretations for long video segments were able to yield performance increases of about 70 and, in addition, proved to be more robust to different severe scenarios of classification errors.
Line-Sweep: Cross-Ratio For Wide-Baseline Matching and 3D Reconstruction
Authors: Srikumar Ramalingam, Michel Antunes, Dan Snow, Gim Hee Lee, Sudeep Pillai
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Reconstruction

We propose a simple and useful idea based on cross-ratio constraint for wide-baseline matching and 3D reconstruction. Most existing methods exploit feature points and planes from images. Lines have always been considered notorious for both matching and reconstruction due to the lack of good line descriptors. We propose a method to generate and match new points using virtual lines constructed using pairs of keypoints, which are obtained using standard feature point detectors. We use cross-ratio constraints to obtain an initial set of new point matches, which are subsequently used to obtain line correspondences. We develop a method that works for both calibrated and uncalibrated camera configurations. We show compelling line-matching and large-scale 3D reconstruction.
Simplified Mirror-Based Camera Pose Computation via Rotation Averaging
Authors: Gucan Long, Laurent Kneip, Xin Li, Xiaohu Zhang, Qifeng Yu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Camera Calibration, Pose Estimation

We propose a novel approach to compute the camera pose with respect to a reference object given only mirrored views. The latter originate from a planar mirror at different unknown poses. This problem is highly relevant in several extrinsic camera calibration scenarios, where the camera cannot see the reference object directly. In contrast to numerous existing methods, our approach does not employ the fixed axis rotation constraint, but represents a more elegant formulation as a rotation averaging problem. Our theoretical contribution extends the applicability of rotation averaging to a more general case, and enables mirror-based pose estimation in closed-form under the chordal L2-metric, or in an outlier-robust way by employing iterative L1-norm averaging. We demonstrate the advantages of our approach on both synthetic and real data, and show how the method can be applied to calibrate the non-overlapping pair of cameras of a common smart phone.
On the Relationship Between Visual Attributes and Convolutional Networks
Authors: Victor Escorcia, Juan Carlos Niebles, Bernard Ghanem
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Attribute, Object Recognition, Retrieval, Zero-Shot Learning

One of the cornerstone  principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from `simpler' ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we  uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.
Saliency Detection by Multi-Context Deep Learning
Authors: Rui Zhao, Wanli Ouyang, Hongsheng Li, Xiaogang Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deep Learning, image-classification, Image Classification, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance. This issue raises a serious problem for conventional approaches. In this paper, we tackle this problem by proposing a multi-context deep learning framework for salient object detection. We employ deep Convolutional Neural Networks to model saliency of objects in images. Global context and local context are both taken into account, and are jointly modeled in a unified multi-context deep learning framework.   To provide a better initialization for training the deep neural networks, we investigate different pre-training strategies, and a task-specific pre-training scheme is designed to make the multi-context modeling suited for saliency detection. Furthermore, recently proposed contemporary deep models in the ImageNet Image Classification Challenge are tested, and their effectiveness in saliency detection are investigated. Our approach is extensively evaluated on five public datasets, and experimental results show significant and consistent improvements over the state-of-the-art methods.
DeepShape: Deep Learned Shape Descriptor for 3D Shape Matching and Retrieval
Authors: Jin Xie, Yi Fang, Fan Zhu, Edward Wong
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Retrieval

Complex geometric structural variations of 3D models usually pose great challenges in 3D shape matching and retrieval. In this paper, we propose a high-level shape feature learning scheme to extract deformation-insensitive feature via a novel discriminative deep auto-encoder. First, we developed a multiscale shape distribution to concisely describe the entire shape of a 3D object. Then, by imposing the Fisher discrimination criterion on the neurons in the hidden layer, we developed a novel discriminative deep auto-encoder for shape feature learning. Finally, the neurons in hidden layers from multiple discriminative auto-encoders are concatenated to form a shape descriptor for 3D shape matching and retrieval. The proposed method is evaluated on the representative datasets with large geometric variations, i.e., Mcgill, SHREC'10 ShapeGoogle datasets. Experimental results on the benchmark datasets demonstrate the effectiveness of the proposed method on the applications of 3D shape matching and retrieval.
Bayesian Adaptive Matrix Factorization With Automatic Model Selection
Authors: Peixian Chen, Naiyan Wang, Nevin L. Zhang, Dit-yan Yeung
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: model, Model Selection

Low-rank matrix factorization has long been recognized as a fundamental problem in many computer vision applications. Nevertheless, the reliability of existing matrix factorization methods is often hard to guarantee due to challenges brought by such model selection issues as selecting the noise model and determining the model capacity. We address these two issues simultaneously in this paper by proposing a robust non-parametric Bayesian adaptive matrix factorization (AMF) model. AMF proposes a new noise model built on the Dirichlet process Gaussian mixture model (DP-GMM) by taking advantage of its high flexibility on component number selection and capability of fitting a wide range of unknown noise. AMF also imposes an automatic relevance determination (ARD) prior on the low-rank factor matrices so that the rank can be determined automatically without the need for enforcing any hard constraint. An efficient variational method is then devised for model inference. We compare AMF with state-of-the-art matrix factorization methods based on data sets ranging from synthetic data to real-world application data. From the results, AMF consistently achieves better or comparable performance.
Joint Action Recognition and Pose Estimation From Video
Authors: Bruce Xiaohan Nie, Caiming Xiong, Song-Chun Zhu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Pose Estimation, Skeleton Based Action Recognition, Temporal Action Localization

Action recognition and pose estimation from video are closely related tasks for understanding human motion, most methods, however, learn separate models and combine them sequentially. In this paper, we propose a framework to integrate training and testing of the two tasks. A spatial- temporal And-Or graph model is introduced to represent action at three scales. Specifically the action is decomposed into poses which are further divided to mid-level ST-parts and then parts. The hierarchical structure of our model captures the geometric and appearance variations of pose at each frame and lateral connections between ST-parts at adjacent frames capture the action-specific motion information. The model parameters for three scales are learned discriminatively, and action labels and poses are efficiently inferred by dynamic programming. Experiments demonstrate that our approach achieves state-of-art accuracy in action recognition while also improving pose estimation.
Fast Action Proposals for Human Action Detection and Search
Authors: Gang Yu, Junsong Yuan
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Detection, Video Segmentation, Video Semantic Segmentation

In this paper we target at generating generic action proposals in unconstrained videos. Each action proposal corresponds to a temporal series of spatial bounding boxes, i.e., a spatio-temporal video tube, which has a good potential to locate one human action. Assuming each action is performed by a human with meaningful motion, both appearance and motion cues are utilized to measure the actionness of the video tubes. After picking those spatiotemporal paths of high actionness scores, our action proposal generation is formulated as a maximum set coverage problem, where greedy search is performed to select a set of action proposals that can maximize the overall actionness score. Compared with existing action proposal approaches, our action proposals do not rely on video segmentation and can be generated in nearly real-time. Experimental results on two challenging datasets, MSRII and UCF 101, validate the superior performance of our action proposals as well as competitive results on action detection and search.
Joint Multi-Feature Spatial Context for Scene Recognition on the Semantic Manifold
Authors: Xinhang Song, Shuqiang Jiang, Luis Herranz
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Scene Recognition

In the semantic multinomial framework patches and images are modeled as points in a semantic probability simplex. Patch theme models are learned resorting to weak supervision via image labels, which leads the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns is critical to improve the recognition performance in this representation. In this paper, we observe that not only global co-occurrences at the image-level are important, but also different regions have different category co-occurrence patterns. We exploit local contextual relations to address the problem of discovering consistent co-occurrence patterns and removing noisy ones. Our hypothesis is that a less noisy semantic representation, would greatly help the classifier to model consistent co-occurrences and discriminate better between scene categories. An important advantage of modeling features in a semantic space, is that this space is feature independent. Thus, we can combine multiple features and spatial neighbors in the same common space, and formulate the problem as minimizing a context-dependent energy. Experimental results show that exploiting different types of contextual relations consistently improves the recognition accuracy. In particular, larger datasets benefit more from the proposed method, leading to very competitive performance.
Large-Scale Damage Detection Using Satellite Imagery
Authors: Lionel Gueguen, Raffay Hamid
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Satellite imagery is a valuable source of information for assessing damages in distressed areas undergoing a calamity, such as an earthquake or an armed conflict. However, the sheer amount of data required to be inspected for this assessment makes it impractical to do it manually. To address this problem, we present a semi-supervised learning framework for large-scale damage detection in satellite imagery. We present a comparative evaluation of our framework using over 88 million images collected from 4,665 square kilometers from 12 different locations around the world. To enable accurate and efficient damage detection, we introduce a novel use of hierarchical shape features in the bags-of-visual words setting. We analyze how practical factors such as sun, sensor-resolution, and satellite-angle differences impact the effectiveness of our proposed representation, and compare it to five alternative features in multiple learning settings. Finally, we demonstrate through a user-study that our semi-supervised framework results in a ten-fold reduction in human annotation time at a minimal loss in detection accuracy compared to an exhaustive manual inspection.
A Novel Locally Linear KNN Model for Visual Recognition
Authors: Qing-Feng Liu, Chengjun Liu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Density Estimation, Face Recognition, General Classification, Object Recognition, Scene Recognition, Temporal Action Localization

This paper presents a novel locally linear KNN model with the goal of not only developing efficient representation and classification methods, but also establishing a relation between them so as to approximate some classification rules, e.g. the Bayes decision rule. Towards that end, first, the proposed model represents the test sample as a linear combination of all the training samples and derives a new representation by learning the coefficients considering the reconstruction, locality and sparsity constraints.  The theoretical analysis shows that the new representation has the grouping effect of the nearest neighbors, which is able to approximate the "ideal representation". And then the locally linear KNN model based classifier (LLKNNC), which shows its connection to the Bayes decision rule for minimum error in the view of kernel density estimation, is proposed for classification. Besides, the locally linear nearest mean classifier (LLNMC), whose relation to the LLKNNC is just like the nearest mean classifier to the KNN classifier, is also derived. Furthermore, to provide reliable kernel density estimation, the shifted power transformation and the coefficients cut-off method are applied to improve the performance of the proposed method. The effectiveness of the proposed model is evaluated on several visual recognition tasks such as face recognition, scene recognition, object recognition and action recognition. The experimental results show that the proposed model is effective and outperforms some other representative popular methods.
Superpixel Segmentation Using Linear Spectral Clustering
Authors: Zhengqin Li, Jiansheng Chen
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

We present in this paper a superpixel segmentation algorithm called Linear Spectral Clustering (LSC), which produces compact and uniform superpixels with low computational costs. Basically, a normalized cuts formulation of the superpixel segmentation is adopted based on a similarity metric that measures the color similarity and space proximity between image pixels. However, instead of using the traditional eigen-based algorithm, we approximate the similarity metric using a kernel function leading to an explicitly mapping of pixel values and coordinates into a high dimensional feature space. We revisit the conclusion that by appropriately weighting each point in this feature space, the objective functions of weighted K-means and normalized cuts share the same optimum point. As such, it is possible to optimize the cost function of normalized cuts by iteratively applying simple K-means clustering in the proposed feature space. LSC is of linear computational complexity and high memory efficiency and is able to preserve global properties of images. Experimental results show that LSC performs equally well or better than state of the art superpixel segmentation algorithms in terms of several commonly used evaluation metrics in image segmentation.