pwc-archive/papers-with-abstracts huggingface dataset:
Object Proposal by Multi-Branch Hierarchical Segmentation
Authors: Chaoyang Wang, Long Zhao, Shuang Liang, Liqing Zhang, Jinyuan Jia, Yichen Wei
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection, Segmentation

Hierarchical segmentation based object proposal methods have become an important step in modern object detection paradigm. However, standard single-way hierarchical methods are fundamentally flawed in that the errors in early steps cannot be corrected and accumulate. In this work, we propose a novel multi-branch hierarchical segmentation approach that alleviates such problems by learning multiple merging strategies in each step in a complementary manner, such that errors in one merging strategy could be corrected by the others. Our approach achieves the state-of-the-art performance for both object proposal and object detection tasks, comparing to previous object proposal methods.
Ambient Occlusion via Compressive Visibility Estimation
Authors: Wei Yang, Yu Ji, Haiting Lin, Yang Yang, Sing Bing Kang, Jingyi Yu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Compressive Sensing

There has been emerging interest on recovering traditionally challenging intrinsic scene properties. In this paper, we present a novel computational imaging solution for recovering the ambient occlusion (AO) map of an object. AO measures how much light from all different directions can reach a surface point without being blocked by self-occlusions. Previous approaches either require obtaining highly accurate surface geometry or acquiring a large number of images. We adopt a compressive sensing framework that captures the object under strategically coded lighting directions. We show that this incident illumination field exhibits some unique properties suitable for AO recovery: every ray's contribution to the visibility function is binary while their distribution for AO measurement is sparse. This enables a sparsity-prior based solution for iteratively recovering the surface normal, the surface albedo, and the visibility function from a small number of images. To physically implement the scheme, we construct an encodable directional light source using the light field probe. Experiments on synthetic and real scenes show that our approach is both reliable and accurate with significantly reduced size of input.
Shape-Tailored Local Descriptors and Their Application to Segmentation and Tracking
Authors: Naeemullah Khan, Marei Algarni, Anthony Yezzi, Ganesh Sundaramoorthi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object Tracking, Segmentation

 We propose new dense descriptors for texture segmentation. Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients. These scale spaces are defined by Poisson-like partial differential equations. A key property of our new descriptors is that they do not aggregate image data across the boundary of the region, in contrast to existing descriptors based on aggregation of oriented gradients. As an example, we show how the descriptor can be incorporated in a Mumford-Shah energy for texture segmentation. We test our method on several challenging datasets for texture segmentation and textured object tracking. Experiments indicate that our descriptors lead to more accurate segmentation than non-shape dependent descriptors and the state-of-the-art in texture segmentation.
Scalable Object Detection by Filter Compression With Regularized Sparse Coding
Authors: Ting-Hsuan Chao, Yen-Liang Lin, Yin-Hsi Kuo, Winston H. Hsu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: CPU, object-detection, Object Detection

For practical applications, an object detection system requires huge number of classes to meet real world needs. Many successful object detection systems use part-based model which trains several filters (classifiers) for each class to perform multiclass object detection. However, these methods have linear computational complexity in regard to the number of classes and may lead to huge computing time. To solve the problem, some works learn a codebook for the filters and conduct operations only on the codebook to make computational complexity sublinear in regard to the number of classes. But the past studies missed to consider filter characteristics, e.g., filters are weights trained by Support Vector Machine, and rather they applied method such as sparse coding for visual signals' optimization. This misuse results in huge accuracy loss when a large speedup is required. To remedy this shortcoming, we have developed a new method called Regularized Sparse Coding which is designed to reconstruct filter functionality. That is, it reconstructs the ability of filter to produce accurate score for classification. Our method can reconstruct filters by minimizing score map error, while sparse coding reconstructs filters by minimizing appearance error. This different optimization strategy makes our method be able to have small accuracy loss when a large speedup is achieved. On the ILSVRC 2013 dataset, which has 200 classes, this work represents a 16 times speedup using only 1.25% memory on single CPU with 0.04 mAP drop when compared with the original Deformable Part Model. Moreover, parallel computing on GPUs is also applicable for our work to achieve more speedup.
An Improved Deep Learning Architecture for Person Re-Identification
Authors: Ejaz Ahmed, Michael Jones, Tim K. Marks
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deep Learning, Person Re-Identification, Small Data Image Classification

In this work we propose a method for simultaneously learning features and a corresponding similarity metric for person re-identification. We present a deep convolution architecture with layers specially designed to address the problem of re-identification. Given a pair of images as input, our network outputs a similarity value indicating whether the two input images depict the same person. Novel elements of our architecture include a layer that computes cross-input neighborhood differences, which capture local relationships among mid-level features that were computed separately from the two input images. A high-level summary of the outputs of this layer is computed by a layer of patch summary features, which are then spatially integrated in subsequent layers. Our method significantly outperforms the state of the art on both a large data set (CUHK03) and a medium-sized dataset (CUHK01), and it is resistant to overfitting. We also demonstrate that by initially training on an unrelated large data set before fine tuning on a small target data set, our network can achieve results comparable to the state of the art even on the small data set (VIPeR).
Understanding Classifier Errors by Examining Influential Neighbors
Authors: Mayank Kabra, Alice Robie, Kristin Branson
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Modern supervised learning algorithms can learn very accurate and complex discriminating functions. But when these classifiers fail, this complexity can also be a drawback because there is no easy, intuitive way to diagnose why they are failing and remedy the problem. This important question has received little attention. To address this problem, we propose a novel method to analyze and understand a classifier's errors. Our method centers around a measure of how much influence a training example has on the classifier's prediction for a test example. To understand why a classifier is mispredicting the label of a given test example, the user can find and review the most influential training examples that caused this misprediction, allowing them to focus their attention on relevant areas of the data space. This will aid the user in determining if and how the training data is inconsistently labeled or lacking in diversity, or if the feature representation is insufficient. As computing the influence of each training example is computationally impractical, we propose a novel distance metric to approximate influence for boosting classifiers that is fast enough to be used interactively. We also show several novel use paradigms of our distance metric. Through experiments, we show that it can be used to find incorrectly or inconsistently labeled training examples, to find specific areas of the data space that need more training data, and to gain insight into which features are missing from the current representation.
Riemannian Coding and Dictionary Learning: Kernels to the Rescue
Authors: Mehrtash Harandi, Mathieu Salzmann
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Dictionary Learning

While sparse coding on non-flat Riemannian manifolds has recently become increasingly popular, existing solutions either are dedicated to specific manifolds, or rely on optimization problems that are difficult to solve, especially when it comes to dictionary learning. In this paper, we propose to make use of kernels to perform coding and dictionary learning on Riemannian manifolds. To this end, we introduce a general Riemannian coding framework with its kernel-based counterpart. This lets us (i) generalize beyond the special case of sparse coding; (ii) introduce efficient solutions to two coding schemes; (iii) learn the kernel parameters; (iv) perform unsupervised and supervised dictionary learning in a much simpler manner than previous Riemannian coding methods. We demonstrate the effectiveness of our approach on three different types of non-flat manifolds, and illustrate its generality by applying it to Euclidean spaces, which also are Riemannian manifolds.
Scalable Structure From Motion for Densely Sampled Videos
Authors: Benjamin Resch, Hendrik P. A. Lensch, Oliver Wang, Marc Pollefeys, Alexander Sorkine-Hornung
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Camera Pose Estimation, Pose Estimation, Simultaneous Localization and Mapping

Videos consisting of thousands of high resolution frames are challenging for existing structure from motion (SfM) and simultaneous-localization and mapping (SLAM) techniques. We present a new approach for simultaneously computing extrinsic camera poses and 3D scene structure that is capable of handling such large volumes of image data. The key insight behind this paper is to effectively exploit coherence in densely sampled video input. Our technical contributions include robust tracking and selection of confident video frames, a novel window bundle adjustment, frame-to-structure verification for globally consistent reconstructions with multi-loop closing, and utilizing efficient global linear camera pose estimation in order to link both consecutive and distant bundle adjustment windows. To our knowledge we describe the first system that is capable of handling high resolution, high frame-rate video data with close to realtime performance. In addition, our approach can robustly integrate data from different video sequences, allowing multiple video streams to be simultaneously calibrated in an efficient and globally optimal way. We demonstrate high quality alignment on large scale challenging datasets, e.g., 2-20 megapixel resolution at frame rates of 25-120 Hz with thousands of frames.
Holistic 3D Scene Understanding From a Single Geo-Tagged Image
Authors: Shenlong Wang, Sanja Fidler, Raquel Urtasun
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Object Detection, object-detection, Object Detection, Pose Estimation, Scene Understanding, Semantic Segmentation

In this paper we are interested in exploiting geographic priors to help outdoor scene understanding. Towards this goal we propose  a holistic approach that reasons jointly about 3D object detection, pose estimation, semantic segmentation as well as depth reconstruction from a single image. Our approach takes  advantage of  large-scale crowd-sourced maps to generate dense geographic, geometric and semantic priors by rendering the 3D world. We demonstrate the effectiveness of our holistic model on the challenging KITTI dataset, and show significant improvements over the baselines in all metrics and tasks.
DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection
Authors: Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Contour Detection, Image Segmentation, Object Recognition, Segmentation, Semantic Segmentation

Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition. The mainstream works to address this problem focus on designing engineered gradient features. In this work, we show that contour detection accuracy can be improved by instead making the use of the deep features learned from convolutional neural networks (CNNs). While rather than using the networks as a blackbox feature extractor, we customize the training strategy by partitioning contour (positive) data into subclasses and fitting each subclass by different model parameters. A new loss function, named positive-sharing loss, in which each subclass shares the loss for the whole positive class, is proposed to learn the parameters. Compared to the sofmax loss function, the proposed one, introduces an extra regularizer to emphasizes the losses for the positive and negative classes, which facilitates to explore more discriminative features. Our experimental results demonstrate that learned deep features can achieve top performance on Berkeley Segmentation Dataset and Benchmark (BSDS500) and obtain competitive cross dataset generalization result on the NYUD dataset.
A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects
Authors: Kai Han, Kwan-Yee K. Wong, Miaomiao Liu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Surface Reconstruction, Transparent objects

This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light. We introduce a simple setup that allows us alter the incident light paths before light rays enter the object, and develop a method for recovering the object surface based on reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the shape of the object nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a complex shape and structure, with unknown and even inhomogeneous refractive index. Experimental results on both synthetic and real data are presented which demonstrate the feasibility and accuracy of our proposed approach.
Line Drawing Interpretation in a Multi-View Context
Authors: Jean-Dominique Favreau, Florent Lafarge, Adrien Bousseau
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Many design tasks involve the creation of new objects in the context of an existing scene. Existing work in computer vision only provides partial support for such tasks. On the one hand, multi-view stereo algorithms allow the reconstruction of real-world scenes, while on the other hand algorithms for line-drawing interpretation do not take context into account. Our work combines the strength of these two domains to interpret line drawings of imaginary objects drawn over photographs of an existing scene. The main challenge we face is to identify the existing 3D structure that correlates with the line drawing while also allowing the creation of new structure that is not present in the real world. We propose a labeling algorithm to tackle this problem, where some of the labels capture dominant orientations of the real scene while a free label allows the discovery of new orientations in the imaginary scene. We illustrate our algorithm by interpreting line drawings for urban planing, home remodeling, furniture design and cultural heritage.
Toward User-Specific Tracking by Detection of Human Shapes in Multi-Cameras
Authors: Chun-Hao Huang, Edmond Boyer, Bibiana do Canto Angonese, Nassir Navab, Slobodan Ilic
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Temporal Sequences

Human shape tracking consists in fitting a template model to temporal sequences of visual observations. It usually comprises an association step, that finds correspondences between the model and the input data, and a deformation step, that fits the model to the observations given correspondences. Most current approaches find their common ground with the Iterative-Closest-Point (ICP) algorithm, which facilitates the association step with local distance considerations. It fails when large deformations occur, and errors in the association tend to propagate over time. In this paper, we propose a discriminative alternative for the association, that leverages random forests to infer correspondences in one shot. It allows for large deformations and prevents tracking errors from accumulating. The approach is successfully integrated to a surface tracking framework that recovers human shapes and poses jointly. When combined with ICP, this discriminative association proves to yield better accuracy in registration, more stability when tracking over time, and faster convergence. Evaluations on existing datasets demonstrate the benefits with respect to the state-of-the-art.
Intra-Frame Deblurring by Leveraging Inter-Frame Camera Motion
Authors: Haichao Zhang, Jianchao Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring, Video Deblurring

Camera motion introduces motion blur, degrading the quality of  video. A video deblurring method is proposed based on two observations: (i) camera motion within capture of each individual frame leads to motion blur; (ii) camera motion between frames yields  inter-frame mis-alignment that can  be exploited for blur removal. The proposed method effectively leverages the information distributed across multiple video frames  due to camera motion, jointly estimating the motion between consecutive frames and blur within each frame. This joint analysis is crucial for achieving effective restoration by leveraging temporal information. Extensive experiments are carried out on  synthetic data as well as real-world blurry videos. Comparisons with several state-of-the-art methods verify the effectiveness of the proposed method.
Hierarchical-PEP Model for Real-World Face Recognition
Authors: Haoxiang Li, Gang Hua
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Recognition, Face Verification, model

Pose variation remains one of the major factors adversely affect the accuracy of real-world face recognition systems. Inspired by the recently proposed probabilistic elastic part (PEP) model and the success of the deep hierarchical architecture in a number of visual tasks, we propose the Hierarchical-PEP model to approach the unconstrained face recognition problem. We apply the PEP model hierarchically to decompose a face image into face parts at different levels of details to build pose-invariant part-based face representations. Following the hierarchy from bottom-up, we stack the face part representations at each layer, discriminatively reduce its dimensionality, and hence aggregate the face part representations layer-by-layer to build a compact and invariant face representation. The Hierarchical-PEP model exploits the fine-grained structures of the face parts at different levels of details to address the pose variations. It is also guided by supervised information in constructing the face part/face representations. We empirically verify the Hierarchical-PEP model on two public benchmarks (i.e., the LFW and YouTube Faces) and a face recognition challenge (i.e., the PaSC grand challenge) for image-based and video-based face verification. The state-of-the-art performance demonstrates the potential of our method.
The Common Self-Polar Triangle of Concentric Circles and Its Application to Camera Calibration
Authors: Haifei Huang, HUI ZHANG, Yiu-ming Cheung
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Camera Calibration

In projective geometry, the common self-polar triangle has often been used to discuss the position relationship of two planar conics. However, there are few researches on the properties of the common self-polar triangle, especially when the two planar conics are special conics. In this paper, we explore the properties of the common self-polar triangle, when the two conics happen to be concentric circles. We show there exist infinite many common self-polar triangles of two concentric circles, and provide a method to locate the vertices of these triangles. By investigating all these triangles, we find that they encode two important properties. The first one is all triangles share one common vertex, and the opposite side of the common vertex lies on the same line, which are the circle center and the line at the infinity of the support plane. The second is all triangles are right triangles. Based on these two properties, the imaged circle center and the varnishing line of support plane can be recovered simultaneously, and many conjugate pairs on vanishing line can be obtained. These allow to induce good constraints on the image of absolute conic. We evaluate two calibration algorithms, whereby accurate results are achieved.  The main contribution of this paper is that we initiate a new perspective to look into circle-based camera calibration problem. We believe that other calibration methods using different circle patterns can benefit from this perspective, especially for the patterns which involve more than two circles.
GMMCP Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Authors: Afshin Dehghan, Shayan Modiri Assari, Mubarak Shah
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Multiple Object Tracking, Object Tracking

Data association is the backbone to many multiple object tracking (MOT) methods. In this paper we formulate data association as a Generalized Maximum Multi Clique problem (GMMCP). We show that this is the ideal case of modeling tracking in real world scenario where all the pairwise relationships between targets in a batch of frames are taken into account. Previous works assume simplified version of our tracker either in problem formulation or problem optimization. However, we propose a solution using GMMCP where no simplification is assumed in either steps. We show that the NP hard problem of GMMCP can be formulated through Binary-Integer Program where for small and medium size MOT problems the solution can be found efficiently. We further propose a speed-up method, employing Aggregated Dummy Nodes for modeling occlusion and miss-detection, which reduces the size of the input graph without using any heuristics. We show that, using the speedup method, our tracker lends itself to real-time implementation which is plausible in many applications. We evaluated our tracker on six challenging sequences of Town Center, TUD-Crossing, TUD-Stadtmitte, Parking-lot 1, Parking-lot 2 and Parking-lot pizza and show favorable improvement against state of art.