pwc-archive/papers-with-abstracts huggingface dataset:
Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification
Authors: Dong Chen, Xudong Cao, Fang Wen, Jian Sun
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Age-Invariant Face Recognition, Face Recognition, Face Verification, regression

Making a high-dimensional (e.g., 100K-dim) feature for face recognition seems not a good idea because it will bring difficulties on consequent training, computation, and storage. This prevents further exploration of the use of a highdimensional feature. In this paper, we study the performance of a highdimensional feature. We first empirically show that high dimensionality is critical to high performance. A 100K-dim feature, based on a single-type Local Binary Pattern (LBP) descriptor, can achieve significant improvements over both its low-dimensional version and the state-of-the-art. We also make the high-dimensional feature practical. With our proposed sparse projection method, named rotated sparse regression, both computation and model storage can be reduced by over 100 times without sacrificing accuracy quality.
Robust Estimation of Nonrigid Transformation for Point Set Registration
Authors: Jiayi Ma, Ji Zhao, Jinwen Tian, Zhuowen Tu, Alan L. Yuille
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

We present a new point matching algorithm for robust nonrigid registration. The method iteratively recovers the point correspondence and estimates the transformation between two point sets. In the first step of the iteration, feature descriptors such as shape context are used to establish rough correspondence. In the second step, we estimate the transformation using a robust estimator called L 2 E. This is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step. The transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. We apply our method to nonrigid sparse image feature correspondence on 2D images and 3D surfaces. Our results quantitatively show that our approach outperforms state-ofthe-art methods, particularly when there are a large number of outliers. Moreover, our method of robustly estimating transformations from correspondences is general and has many other applications.
Representing Videos Using Mid-level Discriminative Patches
Authors: Arpit Jain, Abhinav Gupta, Mikel Rodriguez, Larry S. Davis
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Action Classification, General Classification

representation for videos based on mid-level discriminative spatio-temporal patches. These spatio-temporal patches might correspond to a primitive human action, a semantic object, or perhaps a random but informative spatiotemporal patch in the video. What defines these spatiotemporal patches is their discriminative and representative properties. We automatically mine these patches from hundreds of training videos and experimentally demonstrate that these patches establish correspondence across videos and align the videos for label transfer techniques. Furthermore, these patches can be used as a discriminative vocabulary for action classification where they demonstrate stateof-the-art performance on UCF50 and Olympics datasets.
Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild
Authors: Zhen Cui, Wen Li, Dong Xu, Shiguang Shan, Xilin Chen
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Face Recognition, Face Verification, Metric Learning

In many real-world face recognition scenarios, face images can hardly be aligned accurately due to complex appearance variations or low-quality images. To address this issue, we propose a new approach to extract robust face region descriptors. Specifically, we divide each image (resp. video) into several spatial blocks (resp. spatial-temporal volumes) and then represent each block (resp. volume) by sum-pooling the nonnegative sparse codes of position-free patches sampled within the block (resp. volume). Whitened Principal Component Analysis (WPCA) is further utilized to reduce the feature dimension, which leads to our Spatial Face Region Descriptor (SFRD) (resp. Spatial-Temporal Face Region Descriptor, STFRD) for images (resp. videos). Moreover, we develop a new distance metric learning method for face verification called Pairwise-constrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks (resp. volumes) from an image (resp. a video). Our work achieves the stateof-the-art performances on two real-world datasets LFW and YouTube Faces (YTF) according to the restricted protocol.
Discriminative Sub-categorization
Authors: Minh Hoai, Andrew Zisserman
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering

The objective of this work is to learn sub-categories. Rather than casting this as a problem of unsupervised clustering, we investigate a weakly supervised approach using both positive and negative samples of the category. We make the following contributions: (i) we introduce a new model for discriminative sub-categorization which determines cluster membership for positive samples whilst simultaneously learning a max-margin classifier to separate each cluster from the negative samples; (ii) we show that this model does not suffer from the degenerate cluster problem that afflicts several competing methods (e.g., Latent SVM and Max-Margin Clustering); (iii) we show that the method is able to discover interpretable sub-categories in various datasets. The model is evaluated experimentally over various datasets, and its performance advantages over k-means and Latent SVM are demonstrated. We also stress test the model and show its resilience in discovering sub-categories as the parameters are varied.
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
Authors: Saurabh Gupta, Pablo Arbelaez, Jitendra Malik
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Boundary Detection, Contour Detection, Object, Object Recognition, Scene Classification, Segmentation, Semantic Segmentation, Superpixels

We address the problems of contour detection, bottomup grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset [27]. We propose algorithms for object boundary detection and hierarchical segmentation that generalize the gP b ucm approach of [2] by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We then turn to the problem of semantic segmentation and propose a simple approach that classifies superpixels into the 40 dominant object categories in NYUD2. We use both generic and class-specific features to encode the appearance and geometry of objects. We also show how our approach can be used for scene classification, and how this contextual information in turn improves object recognition. In all of these tasks, we report significant improvements over the state-of-the-art.
Harvesting Mid-level Visual Concepts from Large-Scale Internet Images
Authors: Quannan Li, Jiajun Wu, Zhuowen Tu
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: image-classification, Image Classification

Obtaining effective mid-level representations has become an increasingly important task in computer vision. In this paper, we propose a fully automatic algorithm which harvests visual concepts from a large number of Internet images (more than a quarter of a million) using text-based queries. Existing approaches to visual concept learning from Internet images either rely on strong supervision with detailed manual annotations or learn image-level classifiers only. Here, we take the advantage of having massive wellorganized Google and Bing image data; visual concepts (around 14, 000) are automatically exploited from images using word-based queries. Using the learned visual concepts, we show state-of-the-art performances on a variety of benchmark datasets, which demonstrate the effectiveness of the learned mid-level representations: being able to generalize well to general natural images. Our method shows significant improvement over the competing systems in image classification, including those with strong supervision.
Sample-Specific Late Fusion for Visual Category Recognition
Authors: Dong Liu, Kuan-Ting Lai, Guangnan Ye, Ming-Syan Chen, Shih-Fu Chang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

Late fusion addresses the problem of combining the prediction scores of multiple classifiers, in which each score is predicted by a classifier trained with a specific feature. However, the existing methods generally use a fixed fusion weight for all the scores of a classifier, and thus fail to optimally determine the fusion weight for the individual samples. In this paper, we propose a sample-specific late fusion method to address this issue. Specifically, we cast the problem into an information propagation process which propagates the fusion weights learned on the labeled samples to individual unlabeled samples, while enforcing that positive samples have higher fusion scores than negative samples. In this process, we identify the optimal fusion weights for each sample and push positive samples to top positions in the fusion score rank list. We formulate our problem as a L ? norm constrained optimization problem and apply the Alternating Direction Method of Multipliers for the optimization. Extensive experiment results on various visual categorization tasks show that the proposed method consistently and significantly beats the state-of-the-art late fusion methods. To the best knowledge, this is the first method supporting sample-specific fusion weight learning.
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors
Authors: Keyang Shi, Keze Wang, Jiangbo Lu, Liang Lin
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Segmentation, Object Recognition, Saliency Detection, Semantic Segmentation

Driven by recent vision and graphics applications such as image segmentation and object recognition, assigning pixel-accurate saliency values to uniformly highlight foreground objects becomes increasingly critical. More often, such fine-grained saliency detection is also desired to have a fast runtime. Motivated by these, we propose a generic and fast computational framework called PISA Pixelwise Image Saliency Aggregating complementary saliency cues based on color and structure contrasts with spatial priors holistically. Overcoming the limitations of previous methods often using homogeneous superpixel-based and color contrast-only treatment, our PISA approach directly performs saliency modeling for each individual pixel and makes use of densely overlapping, feature-adaptive observations for saliency measure computation. We further impose a spatial prior term on each of the two contrast measures, which constrains pixels rendered salient to be compact and also centered in image domain. By fusing complementary contrast measures in such a pixelwise adaptive manner, the detection effectiveness is significantly boosted. Without requiring reliable region segmentation or postrelaxation, PISA exploits an efficient edge-aware image representation and filtering technique and produces spatially coherent yet detail-preserving saliency maps. Extensive experiments on three public datasets demonstrate PISA's superior detection accuracy and competitive runtime speed over the state-of-the-arts approaches.
Simultaneous Super-Resolution of Depth and Images Using a Single Camera
Authors: Hee Seok Lee, Kuoung Mu Lee
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: 3D Reconstruction, Camera Pose Estimation, Image Super-Resolution, Pose Estimation, Super-Resolution

In this paper, we propose a convex optimization framework for simultaneous estimation of super-resolved depth map and images from a single moving camera. The pixel measurement error in 3D reconstruction is directly related to the resolution of the images at hand. In turn, even a small measurement error can cause significant errors in reconstructing 3D scene structure or camera pose. Therefore, enhancing image resolution can be an effective solution for securing the accuracy as well as the resolution of 3D reconstruction. In the proposed method, depth map estimation and image super-resolution are formulated in a single energy minimization framework with a convex function and solved efficiently by a first-order primal-dual algorithm. Explicit inter-frame pixel correspondences are not required for our super-resolution procedure, thus we can avoid a huge computation time and obtain improved depth map in the accuracy and resolution as well as highresolution images with reasonable time. The superiority of our algorithm is demonstrated by presenting the improved depth map accuracy, image super-resolution results, and camera pose estimation.
Learning Structured Hough Voting for Joint Object Detection and Occlusion Reasoning
Authors: Tao Wang, Xuming He, Nick Barnes
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, object-detection, Object Detection

We propose a structured Hough voting method for detecting objects with heavy occlusion in indoor environments. First, we extend the Hough hypothesis space to include both object location and its visibility pattern, and design a new score function that accumulates votes for object detection and occlusion prediction. In addition, we explore the correlation between objects and their environment, building a depth-encoded object-context model based on RGB-D data. Particularly, we design a layered context representation and allow image patches from both objects and backgrounds voting for the object hypotheses. We demonstrate that using a data-driven 2.1D representation we can learn visual codebooks with better quality, and more interpretable detection results in terms of spatial relationship between objects and viewer. We test our algorithm on two challenging RGB-D datasets with significant occlusion and intraclass variation, and demonstrate the superior performance of our method.
3D-Based Reasoning with Blocks, Support, and Stability
Authors: Zhaoyin Jia, Andrew Gallagher, Ashutosh Saxena, Tsuhan Chen
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object

3D volumetric reasoning is important for truly understanding a scene. Humans are able to both segment each object in an image, and perceive a rich 3D interpretation of the scene, e.g., the space an object occupies, which objects support other objects, and which objects would, if moved, cause other objects to fall. We propose a new approach for parsing RGB-D images using 3D block units for volumetric reasoning. The algorithm fits image segments with 3D blocks, and iteratively evaluates the scene based on block interaction properties. We produce a 3D representation of the scene based on jointly optimizing over segmentations, block fitting, supporting relations, and object stability. Our algorithm incorporates the intuition that a good 3D representation of the scene is the one that fits the data well, and is a stable, self-supporting (i.e., one that does not topple) arrangement of objects. We experiment on several datasets including controlled and real indoor scenarios. Results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation.
Sampling Strategies for Real-Time Action Recognition
Authors: Feng Shi, Emil Petriu, Robert Laganiere
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Action Recognition, Computational Efficiency, Temporal Action Localization

Local spatio-temporal features and bag-of-features representations have become popular for action recognition. A recent trend is to use dense sampling for better performance. While many methods claimed to use dense feature sets, most of them are just denser than approaches based on sparse interest point detectors. In this paper, we explore sampling with high density on action recognition. We also investigate the impact of random sampling over dense grid for computational efficiency. We present a real-time action recognition system which integrates fast random sampling method with local spatio-temporal features extracted from a Local Part Model. A new method based on histogram intersection kernel is proposed to combine multiple channels of different descriptors. Our technique shows high accuracy on the simple KTH dataset, and achieves state-of-the-art on two very challenging real-world datasets, namely, 93% on KTH, 83.3% on UCF50 and 47.6% on HMDB51.
SCaLE: Supervised and Cascaded Laplacian Eigenmaps for Visual Object Recognition Based on Nearest Neighbors
Authors: Ruobing Wu, Yizhou Yu, Wenping Wang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object Recognition

Recognizing the category of a visual object remains a challenging computer vision problem. In this paper we develop a novel deep learning method that facilitates examplebased visual object category recognition. Our deep learning architecture consists of multiple stacked layers and computes an intermediate representation that can be fed to a nearest-neighbor classifier. This intermediate representation is discriminative and structure-preserving. It is also capable of extracting essential characteristics shared by objects in the same category while filtering out nonessential differences among them. Each layer in our model is a nonlinear mapping, whose parameters are learned through two sequential steps that are designed to achieve the aforementioned properties. The first step computes a discrete mapping called supervised Laplacian Eigenmap. The second step computes a continuous mapping from the discrete version through nonlinear regression. We have extensively tested our method and it achieves state-of-the-art recognition rates on a number of benchmark datasets.
Video Motion Segmentation Using New Adaptive Manifold Denoising Model
Authors: Dijun Luo, Heng Huang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Denoising, Motion Segmentation, Segmentation

Video motion segmentation techniques automatically segment and track objects and regions from videos or image sequences as a primary processing step for many computer vision applications. We propose a novel motion segmentation approach for both rigid and non-rigid objects using adaptive manifold denoising.  We first introduce an adaptive kernel space in which two feature trajectories are mapped into the same point if they belong to the same rigid object. After that, we employ an embedded manifold denoising approach with the adaptive kernel to segment the motion of rigid and non-rigid objects. The major observation is that the non-rigid objects often lie on a smooth manifold with deviations which can be removed by manifold denoising. We also show that performing manifold denoising on the kernel space is equivalent to doing so on its range space, which theoretically justifies the embedded manifold denoising on the adaptive kernel space. Experimental results indicate that our algorithm, named Adaptive Manifold Denoising (AMD), is suitable for both rigid and non-rigid motion segmentation. Our algorithm works well in many cases where several state-of-the-art algorithms fail.
Cut, Glue & Cut: A Fast, Approximate Solver for Multicut Partitioning
Authors: Thorsten Beier, Thorben Kroeger, Jorg H. Kappes, Ullrich Kothe, Fred A. Hamprecht
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

Recently, unsupervised image segmentation has become increasingly popular. Starting from a superpixel segmentation, an edge-weighted region adjacency graph is constructed. Amongst all segmentations of the graph, the one which best conforms to the given image evidence, as measured by the sum of cut edge weights, is chosen.  Since this problem is NP-hard, we propose a new approximate solver based on the move-making paradigm: first, the graph is recursively partitioned into small regions (cut phase). Then, for any two adjacent regions, we consider alternative cuts of these two regions defining possible moves (glue & cut phase). For planar problems, the optimal move can be found, whereas for non-planar problems, efficient approximations exist.  We evaluate our algorithm on published and new benchmark datasets, which we make available here. The proposed algorithm finds segmentations that, as measured by a loss function, are as close to the ground-truth as the global optimum found by exact solvers. It does so significantly faster then existing approximate methods, which is important for large-scale problems.
Neural Decision Forests for Semantic Image Labelling
Authors: Samuel Rota Bulo, Peter Kontschieder
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Representation Learning

In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees. Recent advances of deep learning architectures demonstrate the power of embedding representation learning within the classifier  An idea that is intuitively supported by the hierarchical nature of the decision forest model where the input space is typically left unchanged during training and testing. We bridge this gap by introducing randomized Multi- Layer Perceptrons (rMLP) as new split nodes which are capable of learning non-linear, data-specific representations and taking advantage of them by finding optimal predictions for the emerging child nodes. To prevent overfitting, we i) randomly select the image data fed to the input layer, ii) automatically adapt the rMLP topology to meet the complexity of the data arriving at the node and iii) introduce an l1-norm based regularization that additionally sparsifies the network. The key findings in our experiments on three different semantic image labelling datasets are consistently improved results and significantly compressed trees compared to conventional classification trees.
Pulling Things out of Perspective
Authors: Lubor Ladicky, Jianbo Shi, Marc Pollefeys
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Depth Estimation, Semantic Segmentation

The limitations of current state-of-the-art methods for single-view depth estimation and semantic segmentations are closely tied to the property of perspective geometry, that the perceived size of the objects scales inversely with the distance.  In this paper, we show that we can use this property to reduce the learning of a pixel-wise depth classifier to a much simpler classifier predicting only the likelihood of a pixel being at an arbitrarily fixed canonical depth. The likelihoods for any other depths can be obtained by applying the same classifier after appropriate image manipulations. Such transformation of the problem to the canonical depth removes the training data bias towards certain depths and the effect of perspective. The approach can be straight-forwardly generalized to multiple semantic classes, improving both depth estimation and semantic segmentation performance by directly targeting the weaknesses of independent approaches. Conditioning the semantic label on the depth provides a way to align the data to their physical scale, allowing to learn a more discriminative classifier. Conditioning depth on the semantic class helps the classifier to distinguish between ambiguities of the otherwise ill-posed problem.  We tested our algorithm on the KITTI road scene dataset and NYU2 indoor dataset and obtained obtained results that significantly outperform current state-of-the-art in both single-view depth and semantic segmentation domain.