pwc-archive/papers-with-abstracts huggingface dataset:
Person Count Localization in Videos From Noisy Foreground and Detections
Authors: Sheng Chen, Alan Fern, Sinisa Todorovic
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Foreground Segmentation, Human Detection, Video Understanding

This paper formulates and presents a solution to a new problem called person count localization. Given a video of a crowded scene, our goal is to output for each frame a set of: 1) Detections optimally covering both isolated individuals and cluttered groups of people; and 2) Counts of people inside these detections. This problem is a middle-ground between frame-level person counting, which does not localize counts, and person detection aimed at perfectly localizing people with count-one detections.  Our problem formulation is important for a wide range of domains, where people appear frequently under severe occlusion within a crowd. As these crowds are often visually distinct from the rest of the scene, they can be viewed as ``visual phrases'' whose spatially tight localization and count assignment could facilitate higher-level video understanding. For count localization, we specify a novel framework of iterative error-driven revisions of a flow graph derived from noisy input of people detections and foreground segmentation. Each iteration creates and solves an integer program for count localization based on iterative revisions of the flow graph. The graph revisions are based on detected violations of basic integrity constraints. They in turn trigger learned modifications to the graph aimed at reducing noise in input features. For evaluation, we introduce a new metric that measures both count precision and localization of our approach on American football and pedestrian videos.
Good Features to Track for Visual SLAM
Authors: Guangcong Zhang, Patricio A. Vela
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Not all measured features in SLAM/SfM contribute to accurate localization during the estimation process, thus it is sensible to utilize only those that do. This paper describes a method for selecting a subset of features that are of high utility for localization in the SLAM/SfM estimation process.  It is derived by examining the observability of SLAM and, being complimentary to the estimation process, it easily integrates into existing SLAM systems.  The measure of estimation utility is formulated with temporal and instantaneous observability indices. Efficient computation strategies for the observability indices are described based on incremental singular value decomposition (SVD) and greedy selection for the temporal and instantaneous observability indices, respectively.  The greedy selection is near-optimal since the observability index is (approximately) submodular.  The proposed method improves localization and data association. Controlled synthetic experiments with ground truth demonstrate the improved localization accuracy, and real-time SLAM experiments demonstrate the improved data association.
Discovering States and Transformations in Image Collections
Authors: Phillip Isola, Joseph J. Lim, Edward H. Adelson
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object

Objects in visual scenes come in a rich variety of transformed states. A few classes of transformation have been heavily studied in computer vision: mostly simple, parametric changes in color and geometry. However, transformations in the physical world occur in many more flavors, and they come with semantic meaning: e.g., bending, folding, aging, etc. The transformations an object can undergo tell us about its physical and functional properties. In this paper, we introduce a dataset of objects, scenes, and materials, each of which is found in a variety of transformed states. Given a novel collection of images, we show how to explain the collection in terms of the states and transformations it depicts. Our system works by generalizing across object classes: states and transformations learned on one set of objects are used to interpret the image collection for an entirely new object class.
Generalized Deformable Spatial Pyramid: Geometry-Preserving Dense Correspondence Estimation
Authors: Junhwa Hur, Hwasup Lim, Changsoo Park, Sang Chul Ahn
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We present a Generalized Deformable Spatial Pyramid (GDSP) matching algorithm for calculating the dense correspondence between a pair of images with large appearance variations. The main challenges of the problem generally originate in appearance dissimilarities and geometric variations between images. To address these challenges, we improve the existing Deformable Spatial Pyramid (DSP) model by generalizing the search space and devising the spatial smoothness. The former is leveraged by rotations and scales, and the latter simultaneously considers dependencies between high-dimensional labels through the pyramid structure. Our spatial regularization in the high-dimensional space enables our model to effectively preserve the meaningful geometry of objects in the input images while allowing for a wide range of geometry variations such as perspective transform and non-rigid deformation. The experimental results on public datasets and challenging scenarios show that our method outperforms the state-of-the-art methods both qualitatively and quantitatively.
Classifier Adaptation at Prediction Time
Authors: Amelie Royer, Christoph H. Lampert
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object Categorization, Prediction

Classifiers for object categorization are usually evaluated by their accuracy on a set of i.i.d. test examples. This provides us with an estimate of the expected error when applying the classifiers to a single new image. In real application, however, classifiers are rarely only used for a single image and then discarded. Instead, they are applied sequentially to many images, and these are typically not i.i.d. samples from a fixed data distribution, but they carry dependencies and their class distribution varies over time. In this work, we argue that the phenomenon of correlated data at prediction time is not a nuisance, but a blessing in disguise. We describe a probabilistic method for adapting classifiers at prediction time without having to retrain them. We also introduce a framework for creating realistically distributed image sequences, which offers a way to benchmark classifier adaptation methods, such as the one we propose. Experiments on the ILSVRC2010 and ILSVRC2012 datasets show that adapting object classification systems at prediction time can significantly reduce their error rate, even with no additional human feedback.
Phase-Based Frame Interpolation for Video
Authors: Simone Meyer, Oliver Wang, Henning Zimmer, Max Grosse, Alexander Sorkine-Hornung
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: global-optimization, Image Generation, Optical Flow Estimation

Standard approaches to computing interpolated (in-between) frames in a video sequence require accurate pixel correspondences between images e.g. using optical flow. We present an efficient alternative by leveraging recent developments in phase-based methods that represent motion in the phase shift of individual pixels. This concept allows in-between images to be generated by simple per-pixel phase modification, without the need for any form of explicit correspondence estimation. Up until now, such methods have been limited in the range of motion that can be interpolated, which fundamentally restricts their usefulness. In order to reduce these limitations, we introduce a novel, bounded phase shift correction method that combines phase information across the levels of a multi-scale pyramid. Additionally, we propose extensions for phase-based image synthesis that yield smoother transitions between the interpolated images. Our approach avoids expensive global optimization typical of optical flow methods, and is both simple to implement and easy to parallelize. This allows us to interpolate frames at a fraction of the computational cost of traditional optical flow-based solutions, while achieving similar quality and in some cases even superior results. Our method fails gracefully in difficult interpolation settings, e.g., significant appearance changes, where flow-based methods often introduce serious visual artifacts. Due to its efficiency, our method is especially well suited for frame interpolation and retiming of high resolution, high frame rate video.
Absolute Pose for Cameras Under Flat Refractive Interfaces
Authors: Sebastian Haner, Kalle Astrom
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper studies the problem of determining the absolute pose of a perspective camera observing a scene through a known refractive plane, the flat boundary between transparent media with different refractive indices. Efficient minimal solvers are developed for the 2D, known orientation and known rotation axis cases, and near-minimal solvers for the general calibrated and unknown focal length cases. We show that ambiguities in the equations of Snell's law give rise to a large number of false solutions, increasing the complexity of the problem. Evaluation of the solvers on both synthetic and real data show excellent numerical performance, and the necessity of explicitly modelling refraction to obtain accurate pose estimates.
Protecting Against Screenshots: An Image Processing Approach
Authors: Alex Yong-Sang Chia, Udana Bandara, Xiangyu Wang, Hiromi Hirano
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Motivated by reasons related to data security and privacy, we propose a method to limit meaningful visual contents of a display from being captured by screenshots. Traditional methods take a system architectural approach to protect against screenshots. We depart from this framework, and instead exploit image processing techniques to distort visual data of a display and present the distorted data to the viewer. Given that a screenshot captures distorted visual contents, it yields limited useful data. We exploit the human visual system to empower viewers to automatically and mentally recover the distorted contents into a meaningful form in real-time. Towards this end, we leverage on findings from psychological studies which show that blending of visual information from recent and current fixations enables human to form meaningful representation of a scene. We model this blending of information by an additive process, and exploit this to design a visual contents distortion algorithm that supports real-time contents recovery by the human visual system. Our experiments and user study demonstrate the feasibility of our method to allow viewers to readily interpret visual contents of a display, while limiting meaningful contents from being captured by screenshots.
Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction
Authors: Ijaz Akhter, Michael J. Black
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

Estimating 3D human pose from 2D joint locations is central to the analysis of people in images and video. To address the fact that the problem is inherently ill posed, many methods impose a prior over human poses. Unfortunately these priors admit invalid poses because they do not model how joint-limits vary with pose. Here we make two key contributions. First, we collect a motion capture dataset that explores a wide range of human poses. From this we learn a pose-dependent model of joint limits that forms our prior. Both dataset and prior are available for research purposes. Second, we define a general parametrization of body pose and a new, multi-stage, method to estimate 3D pose from 2D joint locations using an over-complete dictionary of poses. Our method shows good generalization while avoiding impossible poses. We quantitatively compare our method with recent work and show state-of-the-art results on 2D to 3D pose estimation using the CMU mocap dataset. We also show superior results using manual annotations on real images and automatic detections on the Leeds sports pose dataset.
VisKE: Visual Knowledge Extraction and Question Answering by Visual Verification of Relation Phrases
Authors: Fereshteh Sadeghi, Santosh K. Kumar Divvala, Ali Farhadi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Question Answering, Relation, valid

How can we know whether a statement about our world is valid. For example, given a relationship between a pair of entities e.g., `eat(horse, hay)', how can we know whether this relationship is true or false in general. Gathering such knowledge about entities and their relationships is one of the fundamental challenges in knowledge extraction. Most previous works on knowledge extraction have focused purely on text-driven reasoning for verifying relation phrases. In this work, we introduce the problem of visual verification of relation phrases and developed a Visual Knowledge Extraction system called VisKE. Given a verb-based relation phrase between common nouns, our approach assess its validity by jointly analyzing over text and images and reasoning about the spatial consistency of the relative configurations of the entities and the relation involved. Our approach involves no explicit human supervision thereby enabling large-scale analysis. Using our approach, we have already verified over 12000 relation phrases. Our approach has been used to not only enrich existing textual knowledge bases by improving their recall, but also augment open-domain question-answer reasoning.
A Graphical Model Approach for Matching Partial Signatures
Authors: Xianzhi Du, David Doermann, Wael Abd-Almageed
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering

In this paper, we present a novel partial signature matching method using graphical models. Shape context features are extracted from the contour of signatures to capture local variations, and K-means clustering is used to build a visual vocabulary from a set of reference signatures. To describe the signatures, supervised latent Dirichlet allocation is used to learn the latent distributions of the salient regions over the visual vocabulary and hierarchical Dirichlet processes are implemented to infer the number of salient regions needed. Our work is evaluated on three datasets derived from the DS-I Tobacco signature dataset with clean signatures and the DS-II UMD dataset with signatures with different degradations. The results show the effectiveness of the approach for both the partial and full signature matching.
Semi-Supervised Low-Rank Mapping Learning for Multi-Label Classification
Authors: Liping Jing, Liu Yang, Jian Yu, Michael K. Ng
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, General Classification, Missing Labels, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label problems arise in various domains including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community.  However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labeled data or even missing labels. In this paper, we proposed a semi-supervised low-rank mapping (SLRM) model to handle these two challenges. SLRM model takes advantage of the nuclear norm regularization on mapping to effectively capture the label correlations. Meanwhile, it introduces manifold regularizer on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labeled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve SLRM model based on alternating direction method of multipliers and thus it can efficiently deal with large-scale data sets. Experiments on four real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than state-of-the-art methods.
Computationally Bounded Retrieval
Authors: Mohammad Rastegari, Cem Keskin, Pushmeet Kohli, Shahram Izadi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Attribute, Image Retrieval, Retrieval

The increase in size of large image databases makes the problem of efficient retrieval extremely challenging. This is especially true in the case of high dimensional data where even operations like hashing become expensive because of costly projection operators. Unlike most hashing methods that sacrifice accuracy for speed, we propose a novel method that improves the speed of high dimensional image retrieval by several orders of magnitude without any significant drop in performance. To do this, we propose to learn computationally bounded sparse projections for the encoding step. To further increase the accuracy of the method, we add an orthogonality constraint on projections to reduce bit correlation. We then introduce an iterative scheme that jointly optimizes this objective, which helps us obtain fast and efficient projections. We demonstrate this technique on large retrieval databases, specifically ImageNET, GIST1M and SUN-attribute for the task of nearest neighbor retrieval, and show that our method achieves a speed-up of up to a factor of 100 over state-of-the-art methods, while having on-par and in some cases even better accuracy.
Discrete Hyper-Graph Matching
Authors: Junchi Yan, Chao Zhang, Hongyuan Zha, Wei Liu, Xiaokang Yang, Stephen M. Chu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Graph Matching

This paper focuses on the problem of hyper-graph matching, by accounting for both unary and higher-order affinity terms. Our method is in line with the linear approximate framework while the problem is iteratively solved in discrete space. It is empirically found more efficient than many extant continuous methods. Moreover, it avoids unknown accuracy loss by heuristic rounding step from the continuous approaches. Under weak assumptions, we prove the iterative discrete gradient assignment in general will trap into a degenerating case -- an m-circle solution path where m is the order of the problem. A tailored adaptive relaxation mechanism is devised to detect the degenerating case and makes the algorithm converge to a fixed point in discrete space. Evaluations on both synthetic and real-world data corroborate the efficiency of our method.
Rolling Shutter Motion Deblurring
Authors: Shuochen Su, Wolfgang Heidrich
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring

Although motion blur and rolling shutter deformations are closely coupled artifacts in images taken with CMOS image sensors, the two phenomena have so far mostly been treated separately, with deblurring algorithms being unable to handle rolling shutter wobble, and rolling shutter algorithms being incapable of dealing with motion blur.  We propose an approach that delivers sharp and undistorted output given a single rolling shutter motion blurred image. The key to achieving this is a global modeling of the camera motion trajectory, which enables each scanline of the image to be deblurred with the corresponding motion segment. We show the results of the proposed framework through experiments on synthetic and real data.
Learning to Generate Chairs With Convolutional Neural Networks
Authors: Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task.
Accurate Depth Map Estimation From a Lenslet Light Field Camera
Authors: Hae-Gon Jeon, Jaesik Park, Gyeongmin Choe, Jinsun Park, Yunsu Bok, Yu-Wing Tai, In So Kweon
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Depth Estimation

This paper introduces an algorithm that accurately estimates depth maps using a lenslet light field camera. The proposed algorithm estimates the multi-view stereo correspondences with sub-pixel accuracy using the cost volume. The foundation for constructing accurate costs is threefold. First, the sub-aperture images are displaced using the phase shift theorem. Second, the gradient costs are adaptively aggregated using the angular coordinates of the light field. Third, the feature correspondences between the sub-aperture images are used as additional constraints. With the cost volume, the multi-label optimization propagates and corrects the depth map in the weak texture regions. Finally, the local depth map is iteratively refined through fitting the local quadratic function to estimate a non-discrete depth map. Because micro-lens images contain unexpected distortions, a method is also proposed that corrects this error. The effectiveness of the proposed algorithm is demonstrated through challenging real world examples and including comparisons with the performance of advanced depth estimation algorithms.
Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person Re-Identification
Authors: Dapeng Chen, Zejian yuan, Gang Hua, Nanning Zheng, Jingdong Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Learning-To-Rank, Patch Matching, Person Re-Identification

In this paper, we address the person re-identification problem, discovering the correct matches for a probe person image from a set of gallery person images. We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched images for a same person. We introduce at least three contributions to person re-identification. First, we present an explicit polynomial kernel feature map, which is capable of characterizing the similarity information of all pairs of patches between two images, called soft-patch-match, instead of greedily keeping only the best matched patch, and thus more robust. Second, we introduce a mixture of linear similarity functions that is able to discover different soft-patch-matching patterns. Last, we introduce a negative semi-definite regularization over a subset of the weights in the similarity function, which is motivated by the connection between explicit polynomial kernel feature map and the Mahalanobis distance, as well as the sparsity constraint over the parameters to avoid over-fitting. Experimental results over three public benchmarks demonstrate the superiority of our approach.