pwc-archive/papers-with-abstracts huggingface dataset:
Dense Non-rigid Point-Matching Using Random Projections
Authors: Raffay Hamid, Dennis Decoste, Chih-Jen Lin
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We present a robust and efficient technique for matching dense sets of points undergoing non-rigid spatial transformations. Our main intuition is that the subset of points that can be matched with high confidence should be used to guide the matching procedure for the rest. We propose a novel algorithm that incorporates these high-confidence matches as a spatial prior to learn a discriminative subspace that simultaneously encodes both the feature similarity as well as their spatial arrangement. Conventional subspace learning usually requires spectral decomposition of the pair-wise distance matrix across the point-sets, which can become inefficient even for moderately sized problems. To this end, we propose the use of random projections for approximate subspace learning, which can provide significant time improvements at the cost of minimal precision loss. This efficiency gain allows us to iteratively find and remove high-confidence matches from the point sets, resulting in high recall. To show the effectiveness of our approach, we present a systematic set of experiments and results for the problem of dense non-rigid image-feature matching.
Large Displacement Optical Flow from Nearest Neighbor Fields
Authors: Zhuoyuan Chen, Hailin Jin, Zhe Lin, Scott Cohen, Ying Wu
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Motion Estimation, Motion Segmentation, Optical Flow Estimation, Segmentation

We present an optical flow algorithm for large displacement motions. Most existing optical flow methods use the standard coarse-to-fine framework to deal with large displacement motions which has intrinsic limitations. Instead, we formulate the motion estimation problem as a motion segmentation problem. We use approximate nearest neighbor fields to compute an initial motion field and use a robust algorithm to compute a set of similarity transformations as the motion candidates for segmentation. To account for deviations from similarity transformations, we add local deformations in the segmentation process. We also observe that small objects can be better recovered using translations as the motion candidates. We fuse the motion results obtained under similarity transformations and under translations together before a final refinement. Experimental validation shows that our method can successfully handle large displacement motions. Although we particularly focus on large displacement motions in this work, we make no sacrifice in terms of overall performance. In particular, our method ranks at the top of the Middlebury benchmark.
Hallucinated Humans as the Hidden Context for Labeling 3D Scenes
Authors: Yun Jiang, Hema Koppula, Ashutosh Saxena
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Attribute, Object, Scene Understanding

For scene understanding, one popular approach has been to model the object-object relationships. In this paper, we hypothesize that such relationships are only an artifact of certain hidden factors, such as humans. For example, the objects, monitor and keyboard, are strongly spatially correlated only because a human types on the keyboard while watching the monitor. Our goal is to learn this hidden human context (i.e., the human-object relationships), and also use it as a cue for labeling the scenes. We present Infinite Factored Topic Model (IFTM), where we consider a scene as being generated from two types of topics: human configurations and human-object relationships. This enables our algorithm to hallucinate the possible configurations of the humans in the scene parsimoniously. Given only a dataset of scenes containing objects but not humans, we show that our algorithm can recover the human object relationships. We then test our algorithm on the task of attribute and object labeling in 3D scenes and show consistent improvements over the state-of-the-art.
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space
Authors: Masaki Saito, Takayuki Okatani, Koichiro Deguchi
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Computational Efficiency, Optical Flow Estimation, Stereo Matching, Stereo Matching Hand

This paper is concerned with the inference of marginal densities based on MRF models. The optimization algorithms for continuous variables are only applicable to a limited number of problems, whereas those for discrete variables are versatile. Thus, it is quite common to convert the continuous variables into discrete ones for the problems that ideally should be solved in the continuous domain, such as stereo matching and optical flow estimation. In this paper, we show a novel formulation for this continuous-discrete conversion. The key idea is to estimate the marginal densities in the continuous domain by approximating them with mixtures of rectangular densities. Based on this formulation, we derive a mean field (MF) algorithm and a belief propagation (BP) algorithm. These algorithms can correctly handle the case where the variable space is discretized in a non-uniform manner. By intentionally using such a non-uniform discretization, a higher balance between computational efficiency and accuracy of marginal density estimates could be achieved. We present a method for actually doing this, which dynamically discretizes the variable space in a coarse-to-fine manner in the course of the computation. Experimental results show the effectiveness of our approach.
Efficient Large-Scale Structured Learning
Authors: Steve Branson, Oscar Beijbom, Serge Belongie
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Binary Classification, Classification, General Classification, object-detection, Object Detection

We introduce an algorithm, SVM-IS, for structured SVM learning that is computationally scalable to very large datasets and complex structural representations. We show that structured learning is at least as fast-and often much faster-than methods based on binary classification for problems such as deformable part models, object detection, and multiclass classification, while achieving accuracies that are at least as good. Our method allows problem-specific structural knowledge to exploited for faster optimization by integrating with a user-defined importance sampling function. We demonstrate fast train times on two challenging largescale datasets for two very different problems: ImageNet for multiclass classification and CUB-200-2011 for deformable part model training. Our method is shown to be 10-50 times faster than SVMstruct for cost-sensitive multiclass classification while being about as fast as the fastest 1-vs-all methods for multiclass classification. For deformable part model training, it is shown to be 50-1000 times faster than methods based on SVMstruct, mining hard negatives, and Pegasos-style stochastic gradient descent. Source code of our method is publicly available.
Relative Volume Constraints for Single View 3D Reconstruction
Authors: Eno Toppe, Claudia Nieuwenhuis, Daniel Cremers
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: 3D Reconstruction, Object, Single-View 3D Reconstruction

We introduce the concept of relative volume constraints in order to account for insufficient information in the reconstruction of 3D objects from a single image. The key idea is to formulate a variational reconstruction approach with shape priors in form of relative depth profiles or volume ratios relating object parts. Such shape priors can easily be derived either from a user sketch or from the object's shading profile in the image. They can handle textured or shadowed object regions by propagating information. We propose a convex relaxation of the constrained optimization problem which can be solved optimally in a few seconds on graphics hardware. In contrast to existing single view reconstruction algorithms, the proposed algorithm provides substantially more flexibility to recover shape details such as self-occlusions, dents and holes, which are not visible in the object silhouette.
Uncalibrated Photometric Stereo for Unknown Isotropic Reflectances
Authors: Feng Lu, Yasuyuki Matsushita, Imari Sato, Takahiro Okabe, Yoichi Sato
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We propose an uncalibrated photometric stereo method that works with general and unknown isotropic reflectances. Our method uses a pixel intensity profile, which is a sequence of radiance intensities recorded at a pixel across multi-illuminance images. We show that for general isotropic materials, the geodesic distance between intensity profiles is linearly related to the angular difference of their surface normals, and that the intensity distribution of an intensity profile conveys information about the reflectance properties, when the intensity profile is obtained under uniformly distributed directional lightings. Based on these observations, we show that surface normals can be estimated up to a convex/concave ambiguity. A solution method based on matrix decomposition with missing data is developed for a reliable estimation. Quantitative and qualitative evaluations of our method are performed using both synthetic and real-world scenes.
Image Understanding from Experts' Eyes by Modeling Perceptual Skill of Diagnostic Reasoning Processes
Authors: Rui Li, Pengcheng Shi, Anne R. Haake
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Diagnostic

Eliciting and representing experts' remarkable perceptual capability of locating, identifying and categorizing objects in images specific to their domains of expertise will benefit image understanding in terms of transferring human domain knowledge and perceptual expertise into image-based computational procedures. In this paper, we present a hierarchical probabilistic framework to summarize the stereotypical and idiosyncratic eye movement patterns shared within 11 board-certified dermatologists while they are examining and diagnosing medical images. Each inferred eye movement pattern characterizes the similar temporal and spatial properties of its corresponding segments of the experts' eye movement sequences. We further discover a subset of distinctive eye movement patterns which are commonly exhibited across multiple images. Based on the combinations of the exhibitions of these eye movement patterns, we are able to categorize the images from the perspective of experts' viewing strategies. In each category, images share similar lesion distributions and configurations. The performance of our approach shows that modeling physicians' diagnostic viewing behaviors informs about medical images' understanding to correct diagnosis.
Robust Discriminative Response Map Fitting with Constrained Local Models
Authors: Akshay Asthana, Stefanos Zafeiriou, Shiyang Cheng, Maja Pantic
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: regression

We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms stateof-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1 second per image. To facilitate future comparisons, we release the MATLAB code acand the pretrained models for research purposes.
Unconstrained Monocular 3D Human Pose Estimation by Action Detection and Cross-Modality Regression Forest
Authors: Tsz-Ho Yu, Tae-Kyun Kim, Roberto Cipolla
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: 2D Pose Estimation, 3D Human Pose Estimation, Action Detection, Monocular 3D Human Pose Estimation, Pose Estimation, regression, Video Action Detection

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts.
Revisiting Depth Layers from Occlusions
Authors: Adarsh Kowdle, Andrew Gallagher, Tsuhan Chen
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object

In this work, we consider images of a scene with a moving object captured by a static camera. As the object (human or otherwise) moves about the scene, it reveals pairwise depth-ordering or occlusion cues. The goal of this work is to use these sparse occlusion cues along with monocular depth occlusion cues to densely segment the scene into depth layers. We cast the problem of depth-layer segmentation as a discrete labeling problem on a spatiotemporal Markov Random Field (MRF) that uses the motion occlusion cues along with monocular cues and a smooth motion prior for the moving object. We quantitatively show that depth ordering produced by the proposed combination of the depth cues from object motion and monocular occlusion cues are superior to using either feature independently, and using a na??ve combination of the features.
Efficient Object Detection and Segmentation for Fine-Grained Recognition
Authors: Anelia Angelova, Shenghuo Zhu
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, object-detection, Object Detection, Semantic Segmentation

We propose a detection and segmentation algorithm for the purposes of fine-grained recognition. The algorithm first detects low-level regions that could potentially belong to the object and then performs a full-object segmentation through propagation. Apart from segmenting the object, we can also 'zoom in' on the object, i.e. center it, normalize it for scale, and thus discount the effects of the background. We then show that combining this with a state-of-the-art classification algorithm leads to significant improvements in performance especially for datasets which are considered particularly hard for recognition, e.g. birds species. The proposed algorithm is much more efficient than other known methods in similar scenarios [4, 21]. Our method is also simpler and we apply it here to different classes of objects, e.g. birds, flowers, cats and dogs. We tested the algorithm on a number of benchmark datasets for fine-grained categorization. It outperforms all the known state-of-the-art methods on these datasets, sometimes by as much as 11%. It improves the performance of our baseline algorithm by 3-4%, consistently on all datasets. We also observed more than a 4% improvement in the recognition performance on a challenging largescale flower dataset, containing 578 species of flowers and 250,000 images.
Fast Rigid Motion Segmentation via Incrementally-Complex Local Models
Authors: Fernando Flores-Mangas, Allan D. Jepson
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Computational Efficiency, Motion Segmentation, Segmentation

The problem of rigid motion segmentation of trajectory data under orthography has been long solved for nondegenerate motions in the absence of noise. But because real trajectory data often incorporates noise, outliers, motion degeneracies and motion dependencies, recently proposed motion segmentation methods resort to non-trivial representations to achieve state of the art segmentation accuracies, at the expense of a large computational cost. This paper proposes a method that dramatically reduces this cost (by two or three orders of magnitude) with minimal accuracy loss (from 98.8% achieved by the state of the art, to 96.2% achieved by our method on the standard Hopkins 155 dataset). Computational efficiency comes from the use of a simple but powerful representation of motion that explicitly incorporates mechanisms to deal with noise, outliers and motion degeneracies. Subsets of motion models with the best balance between prediction accuracy and model complexity are chosen from a pool of candidates, which are then used for segmentation.
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration
Authors: Peter Welinder, Max Welling, Pietro Perona
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Benchmarking

How many labeled examples are needed to estimate a classifier's performance on a new dataset? We study the case where data is plentiful, but labels are expensive. We show that by making a few reasonable assumptions on the structure of the data, it is possible to estimate performance curves, with confidence bounds, using a small number of ground truth labels. Our approach, which we call Semisupervised Performance Evaluation (SPE), is based on a generative model for the classifier's confidence scores. In addition to estimating the performance of classifiers on new datasets, SPE can be used to recalibrate a classifier by reestimating the class-conditional confidence distributions.
Motionlets: Mid-level 3D Parts for Human Motion Recognition
Authors: Li-Min Wang, Yu Qiao, Xiaoou Tang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Action Recognition, Temporal Action Localization

This paper proposes motionlet, a mid-level and spatiotemporal part, for human motion recognition. Motionlet can be seen as a tight cluster in motion and appearance space, corresponding to the moving process of different body parts. We postulate three key properties of motionlet for action recognition: high motion saliency, multiple scale representation, and representative-discriminative ability. Towards this goal, we develop a data-driven approach to learn motionlets from training videos. First, we extract 3D regions with high motion saliency. Then we cluster these regions and preserve the centers as candidate templates for motionlet. Finally, we examine the representative and discriminative power of the candidates, and introduce a greedy method to select effective candidates. With motionlets, we present a mid-level representation for video, called motionlet activation vector. We conduct experiments on three datasets, KTH, HMDB51, and UCF50. The results show that the proposed methods significantly outperform state-of-the-art methods.
Understanding Indoor Scenes Using 3D Geometric Phrases
Authors: Wongun Choi, Yu-Wei Chao, Caroline Pantofaru, Silvio Savarese
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: General Classification, Object, object-detection, Object Detection, Room Layout Estimation, Scene Classification, Scene Understanding

Visual scene understanding is a difficult problem interleaving object detection, geometric reasoning and scene classification. We present a hierarchical scene model for learning and reasoning about complex indoor scenes which is computationally tractable, can be learned from a reasonable amount of training data, and avoids oversimplification. At the core of this approach is the 3D Geometric Phrase Model which captures the semantic and geometric relationships between objects which frequently co-occur in the same 3D spatial configuration. Experiments show that this model effectively explains scene semantics, geometry and object groupings from a single image, while also improving individual object detections.
Intrinsic Characterization of Dynamic Surfaces
Authors: Tony Tung, Takashi Matsuyama
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: General Classification

This paper presents a novel approach to characterize deformable surface using intrinsic property dynamics. 3D dynamic surfaces representing humans in motion can be obtained using multiple view stereo reconstruction methods or depth cameras. Nowadays these technologies have become capable to capture surface variations in real-time, and give details such as clothing wrinkles and deformations. Assuming repetitive patterns in the deformations, we propose to model complex surface variations using sets of linear dynamical systems (LDS) where observations across time are given by surface intrinsic properties such as local curvatures. We introduce an approach based on bags of dynamical systems, where each surface feature to be represented in the codebook is modeled by a set of LDS equipped with timing structure. Experiments are performed on datasets of real-world dynamical surfaces and show compelling results for description, classification and segmentation.
Detecting Changes in 3D Structure of a Scene from Multi-view Images Captured by a Vehicle-Mounted Camera
Authors: Ken Sakurada, Takayuki Okatani, Koichiro Deguchi
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Change Detection

This paper proposes a method for detecting temporal changes of the three-dimensional structure of an outdoor scene from its multi-view images captured at two separate times. For the images, we consider those captured by a camera mounted on a vehicle running in a city street. The method estimates scene structures probabilistically, not deterministically, and based on their estimates, it evaluates the probability of structural changes in the scene, where the inputs are the similarity of the local image patches among the multi-view images. The aim of the probabilistic treatment is to maximize the accuracy of change detection, behind which there is our conjecture that although it is difficult to estimate the scene structures deterministically, it should be easier to detect their changes. The proposed method is compared with the methods that use multi-view stereo (MVS) to reconstruct the scene structures of the two time points and then differentiate them to detect changes. The experimental results show that the proposed method outperforms such MVS-based methods.
Part-Based Visual Tracking with Online Latent Structural Learning
Authors: Rui Yao, Qinfeng Shi, Chunhua Shen, Yanning Zhang, Anton Van Den Hengel
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Structured Prediction, Visual Tracking

Despite many advances made in the area, deformable targets and partial occlusions continue to represent key problems in visual tracking. Structured learning has shown good results when applied to tracking whole targets, but applying this approach to a part-based target model is complicated by the need to model the relationships between parts, and to avoid lengthy initialisation processes. We thus propose a method which models the unknown parts using latent variables. In doing so we extend the online algorithm pegasos to the structured prediction case (i.e., predicting the location of the bounding boxes) with latent part variables. To better estimate the parts, and to avoid over-fitting caused by the extra model complexity/capacity introduced by the parts, we propose a two-stage training process, based on the primal rather than the dual form. We then show that the method outperforms the state-of-the-art (linear and non-linear kernel) trackers.