pwc-archive/papers-with-abstracts huggingface dataset:
Depth Super Resolution by Rigid Body Self-Similarity in 3D
Authors: Michael Hornacek, Christoph Rhemann, Margrit Gelautz, Carsten Rother
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Super-Resolution, Super-Resolution

We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of highresolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patchwise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of 'single-image' super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of PatchMatch. A third contribution is a simple, yet effective patch upscaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars.
SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning
Authors: David Weiss, Ben Taskar
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, Re-Ranking, Segmentation, Semantic Segmentation

We propose SCALPEL, a flexible method for object segmentation that integrates rich region-merging cues with midand high-level information about object layout, class, and scale into the segmentation process. Unlike competing approaches, SCALPEL uses a cascade of bottom-up segmentation models that is capable of learning to ignore boundaries early on, yet use them as a stopping criterion once the object has been mostly segmented. Furthermore, we show how such cascades can be learned efficiently. When paired with a novel method that generates better localized shape priors than our competitors, our method leads to a concise, accurate set of segmentation proposals; these proposals are more accurate on the PASCAL VOC2010 dataset than state-of-the-art methods that use re-ranking to filter much larger bags of proposals. The code for our algorithm is available online.
Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization
Authors: Marcus A. Brubaker, Andreas Geiger, Raquel Urtasun
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Visual Odometry

In this paper we propose an affordable solution to selflocalization, which utilizes visual odometry and road maps as the only inputs. To this end, we present a probabilistic model as well as an efficient approximate inference algorithm, which is able to utilize distributed computation to meet the real-time requirements of autonomous systems. Because of the probabilistic nature of the model we are able to cope with uncertainty due to noisy visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, we are able to localize a vehicle up to 3m after only a few seconds of driving on maps which contain more than 2,150km of drivable roads.
Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories
Authors: Michele Fenzi, Laura Leal-Taixe, Bodo Rosenhahn, Jorn Ostermann
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Pose Estimation, regression

In this paper, we propose a method for learning a class representation that can return a continuous value for the pose of an unknown class instance using only 2D data and weak 3D labelling information. Our method is based on generative feature models, i.e., regression functions learnt from local descriptors of the same patch collected under different viewpoints. The individual generative models are then clustered in order to create class generative models which form the class representation. At run-time, the pose of the query image is estimated in a maximum a posteriori fashion by combining the regression functions belonging to the matching clusters. We evaluate our approach on the EPFL car dataset [17] and the Pointing'04 face dataset [8]. Experimental results show that our method outperforms by 10% the state-of-the-art in the first dataset and by 9% in the second.
Event Retrieval in Large Video Collections with Circulant Temporal Encoding
Authors: Jerome Revaud, Matthijs Douze, Cordelia Schmid, Herve Jegou
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Copy Detection, Quantization, Retrieval

This paper presents an approach for large-scale event retrieval. Given a video clip of a specific event, e.g., the wedding of Prince William and Kate Middleton, the goal is to retrieve other videos representing the same event from a dataset of over 100k videos. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. Furthermore, we extend product quantization to complex vectors in order to compress our descriptors, and to compare them in the compressed domain. Our method outperforms the state of the art both in search quality and query time on two large-scale video benchmarks for copy detection, T RECVID and CC WEB . Finally, we introduce a challenging dataset for event retrieval, EVVE, and report the performance on this dataset.
Looking Beyond the Image: Unsupervised Learning for Object Saliency and Detection
Authors: Parthipan Siva, Chris Russell, Tao Xiang, Lourdes Agapito
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object

We propose a principled probabilistic formulation of object saliency as a sampling problem. This novel formulation allows us to learn, from a large corpus of unlabelled images, which patches of an image are of the greatest interest and most likely to correspond to an object. We then sample the object saliency map to propose object locations. We show that using only a single object location proposal per image, we are able to correctly select an object in over 42% of the images in the P ASCAL VOC 2007 dataset, substantially outperforming existing approaches. Furthermore, we show that our object proposal can be used as a simple unsupervised approach to the weakly supervised annotation problem. Our simple unsupervised approach to annotating objects of interest in images achieves a higher annotation accuracy than most weakly supervised approaches.
Selective Transfer Machine for Personalized Facial Action Unit Detection
Authors: Wen-Sheng Chu, Fernando de la Torre, Jeffery F. Cohn
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Action Unit Detection, Facial Action Unit Detection, Transductive Learning

Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. They neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) and behavior. Individual differences can dramatically influence how well generic classifiers generalize to previously unseen persons. While a possible solution would be to train person-specific classifiers, that often is neither feasible nor theoretically compelling. The alternative that we propose is to personalize a generic classifier in an unsupervised manner (no additional labels for the test subjects are required). We introduce a transductive learning method, which we refer to Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific biases. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2]. STM outperformed generic classifiers in all.
Procrustean Normal Distribution for Non-rigid Structure from Motion
Authors: Minsik Lee, Jungchan Cho, Chong-Ho Choi, Songhwai Oh
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Non-rigid structure from motion is a fundamental problem in computer vision, which is yet to be solved satisfactorily. The main difficulty of the problem lies in choosing the right constraints for the solution. In this paper, we propose new constraints that are more effective for non-rigid shape recovery. Unlike the other proposals which have mainly focused on restricting the deformation space using rank constraints, our proposal constrains the motion parameters so that the 3D shapes are most closely aligned to each other, which makes the rank constraints unnecessary. Based on these constraints, we define a new class of probability distribution called the Procrustean normal distribution and propose a new NRSfM algorithm, EM-PND. The experimental results show that the proposed method outperforms the existing methods, and it works well even if there is no temporal dependence between the observed samples.
Blur Processing Using Double Discrete Wavelet Transform
Authors: Yi Zhang, Keigo Hirakawa
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Deblurring

We propose a notion of double discrete wavelet transform (DDWT) that is designed to sparsify the blurred image and the blur kernel simultaneously. DDWT greatly enhances our ability to analyze, detect, and process blur kernels and blurry images--the proposed framework handles both global and spatially varying blur kernels seamlessly, and unifies the treatment of blur caused by object motion, optical defocus, and camera shake. To illustrate the potential of DDWT in computer vision and image processing, we develop example applications in blur kernel estimation, deblurring, and near-blur-invariant image feature extraction.
Video Enhancement of People Wearing Polarized Glasses: Darkening Reversal and Reflection Reduction
Authors: Mao Ye, Cha Zhang, Ruigang Yang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: General Classification, Video Enhancement

With the wide-spread of consumer 3D-TV technology, stereoscopic videoconferencing systems are emerging. However, the special glasses participants wear to see 3D can create distracting images. This paper presents a computational framework to reduce undesirable artifacts in the eye regions caused by these 3D glasses. More specifically, we add polarized filters to the stereo camera so that partial images of reflection can be captured. A novel Bayesian model is then developed to describe the imaging process of the eye regions including darkening and reflection, and infer the eye regions based on Classification ExpectationMaximization (EM). The recovered eye regions under the glasses are brighter and with little reflections, leading to a more nature videoconferencing experience. Qualitative evaluations and user studies are conducted to demonstrate the substantial improvement our approach can achieve.
Joint Geodesic Upsampling of Depth Images
Authors: Ming-Yu Liu, Oncel Tuzel, Yuichi Taguchi
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Sensor Fusion

We propose an algorithm utilizing geodesic distances to upsample a low resolution depth image using a registered high resolution color image. Specifically, it computes depth for each pixel in the high resolution image using geodesic paths to the pixels whose depths are known from the low resolution one. Though this is closely related to the all-pairshortest-path problem which has O(n lalog n) complexity, we develop a novel approximation algorithm whose complexity grows linearly with the image size and achieve realtime performance. We compare our algorithm with the state of the art on the benchmark dataset and show that our approach provides more accurate depth upsampling with fewer artifacts. In addition, we show that the proposed algorithm is well suited for upsampling depth images using binary edge maps, an important sensor fusion application.
Discriminative Re-ranking of Diverse Segmentations
Authors: Payman Yadollahpour, Dhruv Batra, Gregory Shakhnarovich
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Segmentation, Re-Ranking, Segmentation, Semantic Segmentation

This paper introduces a two-stage approach to semantic image segmentation. In the first stage a probabilistic model generates a set of diverse plausible segmentations. In the second stage, a discriminatively trained re-ranking model selects the best segmentation from this set. The re-ranking stage can use much more complex features than what could be tractably used in the probabilistic model, allowing a better exploration of the solution space than possible by simply producing the most probable solution from the probabilistic model. While our proposed approach already achieves state-of-the-art results (48.1%) on the challenging VOC 2012 dataset, our machine and human analyses suggest that even larger gains are possible with such an approach.
Incorporating User Interaction and Topological Constraints within Contour Completion via Discrete Calculus
Authors: Jia Xu, Maxwell D. Collins, Vikas Singh
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Interactive Segmentation

We study the problem of interactive segmentation and contour completion for multiple objects. The form of constraints our model incorporates are those coming from user scribbles (interior or exterior constraints) as well as information regarding the topology of the 2-D space after partitioning (number of closed contours desired). We discuss how concepts from discrete calculus and a simple identity using the Euler characteristic of a planar graph can be utilized to derive a practical algorithm for this problem. We also present specialized branch and bound methods for the case of single contour completion under such constraints. On an extensive dataset of ~ 1000 images, our experiments suggest that a small amount of side knowledge can give strong improvements over fully unsupervised contour completion methods. We show that by interpreting user indications topologically, user effort is substantially reduced.
Shading-Based Shape Refinement of RGB-D Images
Authors: Lap-Fai Yu, Sai-Kit Yeung, Yu-Wing Tai, Stephen Lin
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We present a shading-based shape refinement algorithm which uses a noisy, incomplete depth map from Kinect to help resolve ambiguities in shape-from-shading. In our framework, the partial depth information is used to overcome bas-relief ambiguity in normals estimation, as well as to assist in recovering relative albedos, which are needed to reliably estimate the lighting environment and to separate shading from albedo. This refinement of surface normals using a noisy depth map leads to high-quality 3D surfaces. The effectiveness of our algorithm is demonstrated through several challenging real-world examples.
Active Contours with Group Similarity
Authors: Xiaowei Zhou, Xiaojie Huang, James S. Duncan, Weichuan Yu
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Segmentation, Semantic Segmentation

Active contours are widely used in image segmentation. To cope with missing or misleading features in images, researchers have introduced various ways to model the prior of shapes and use the prior to constrain active contours. However, the shape prior is usually learnt from a large set of annotated data, which is not always accessible in practice. Moreover, it is often doubted that the existing shapes in the training set will be sufficient to model the new instance in the testing image. In this paper, we propose to use the group similarity of object shapes in multiple images as a prior to aid segmentation, which can be interpreted as an unsupervised approach of shape prior modeling. We show that the rank of the matrix consisting of multiple shapes is a good measure of the group similarity of the shapes, and the nuclear norm minimization is a simple and effective way to impose the proposed constraint on existing active contour models. Moreover, we develop a fast algorithm to solve the proposed model by using the accelerated proximal method. Experiments using echocardiographic image sequences acquired from acute canine experiments demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against image defects such as missing boundaries.
Diffusion Processes for Retrieval Revisited
Authors: Michael Donoser, Horst Bischof
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Retrieval

In this paper we revisit diffusion processes on affinity graphs for capturing the intrinsic manifold structure defined by pairwise affinity matrices. Such diffusion processes have already proved the ability to significantly improve subsequent applications like retrieval. We give a thorough overview of the state-of-the-art in this field and discuss obvious similarities and differences. Based on our observations, we are then able to derive a generic framework for diffusion processes in the scope of retrieval applications, where the related work represents specific instances of our generic formulation. We evaluate our framework on several retrieval tasks and are able to derive algorithms that e. g. achieve a 100% bullseye score on the popular MPEG7 shape retrieval data set.
From N to N+1: Multiclass Transfer Incremental Learning
Authors: Ilja Kuzborskij, Francesco Orabona, Barbara Caputo
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Incremental Learning, Object Categorization, Transfer Learning

Since the seminal work of Thrun [17], the learning to learn paradigm has been defined as the ability of an agent to improve its performance at each task with experience, with the number of tasks. Within the object categorization domain, the visual learning community has actively declined this paradigm in the transfer learning setting. Almost all proposed methods focus on category detection problems, addressing how to learn a new target class from few samples by leveraging over the known source. But if one thinks of learning over multiple tasks, there is a need for multiclass transfer learning algorithms able to exploit previous source knowledge when learning a new class, while at the same time optimizing their overall performance. This is an open challenge for existing transfer learning algorithms. The contribution of this paper is a discriminative method that addresses this issue, based on a Least-Squares Support Vector Machine formulation. Our approach is designed to balance between transferring to the new class and preserving what has already been learned on the source models. Extensive experiments on subsets of publicly available datasets prove the effectiveness of our approach.
The SVM-Minus Similarity Score for Video Face Recognition
Authors: Lior Wolf, Noga Levy
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Face Recognition

Face recognition in unconstrained videos requires specialized tools beyond those developed for still images: the fact that the confounding factors change state during the video sequence presents a unique challenge, but also an opportunity to eliminate spurious similarities. Luckily, a major source of confusion in visual similarity of faces is the 3D head orientation, for which image analysis tools provide an accurate estimation. The method we propose belongs to a family of classifierbased similarity scores. We present an effective way to discount pose induced similarities within such a framework, which is based on a newly introduced classifier called SVMminus. The presented method is shown to outperform existing techniques on the most challenging and realistic publicly available video face recognition benchmark, both by itself, and in concert with other methods.
Human Pose Estimation Using Body Parts Dependent Joint Regressors
Authors: Matthias Dantone, Juergen Gall, Christian Leistner, Luc van Gool
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Pose Estimation

In this work, we address the problem of estimating 2d human pose from still images. Recent methods that rely on discriminatively trained deformable parts organized in a tree model have shown to be very successful in solving this task. Within such a pictorial structure framework, we address the problem of obtaining good part templates by proposing novel, non-linear joint regressors. In particular, we employ two-layered random forests as joint regressors. The first layer acts as a discriminative, independent body part classifier. The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This results in a pose estimation framework that takes dependencies between body parts already for joint localization into account and is thus able to circumvent typical ambiguities of tree structures, such as for legs and arms. In the experiments, we demonstrate that our body parts dependent joint regressors achieve a higher joint localization accuracy than tree-based state-of-the-art methods.
A Principled Deep Random Field Model for Image Segmentation
Authors: Pushmeet Kohli, Anton Osokin, Stefanie Jegelka
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We discuss a model for image segmentation that is able to overcome the short-boundary bias observed in standard pairwise random field based approaches. To wit, we show that a random field with multi-layered hidden units can encode boundary preserving higher order potentials such as the ones used in the cooperative cuts model of [11] while still allowing for fast and exact MAP inference. Exact inference allows our model to outperform previous image segmentation methods, and to see the true effect of coupling graph edges. Finally, our model can be easily extended to handle segmentation instances with multiple labels, for which it yields promising results.