pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Sparse Coding With Geometric Prior For Visual Geo-Location
Authors: Raghuraman Gopalan
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Representation Learning

We address the problem of estimating location information of an image using principles from automated representation learning. We pursue a hierarchical sparse coding approach that learns features useful in discriminating images across locations, by initializing it with a geometric prior corresponding to transformations between image appearance space and their corresponding location grouping space using the notion of parallel transport on manifolds. We then extend this approach to account for the availability of heterogeneous data modalities such as geo-tags and videos pertaining to different locations, and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations. We evaluate our approach on several standard datasets such as im2gps, San Francisco and MediaEval2010, and obtain state-of-the-art results.
Joint Vanishing Point Extraction and Tracking
Authors: Till Kroeger, Dengxin Dai, Luc van Gool
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We present a novel vanishing point (VP) detection and tracking algorithm for calibrated monocular image sequences.   Previous VP detection and tracking methods usually assume known camera poses for all frames or detect and track separately. We advance the state-of-the-art by combining VP extraction on a Gaussian sphere with recent advances in multi-target tracking on probabilistic occupancy fields.  The solution is obtained by solving a Linear Program (LP). This enables the joint detection and tracking of multiple VPs over sequences. Unlike existing works we do not need known camera poses, and at the same time avoid detecting and tracking in separate steps. We also propose an extension to enforce VP orthogonality. We augment an existing video dataset consisting of 48 monocular videos with multiple annotated VPs in 14448 frames for evaluation. Although the method is designed for unknown camera poses, it is also helpful in scenarios with known poses, since a multi-frame approach in VP detection helps to regularize in frames with weak VP line support.
Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition
Authors: Hossein Rahmani, Ajmal Mian
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Temporal Action Localization, Transfer Learning

This paper concerns action recognition from unseen and unknown views. We propose unsupervised learning of a non-linear model that  transfers knowledge from multiple views to a canonical view. The proposed Non-linear Knowledge Transfer Model (NKTM) is a deep network, with weight decay and sparsity constraints, which finds a shared high-level virtual path from videos captured from different unknown viewpoints to the same canonical view. The strength of our technique is that we learn a single NKTM for all actions and all camera viewing directions. Thus, NKTM does not require action labels during learning and knowledge of the camera viewpoints during training or testing. NKTM is learned once only from dense trajectories of synthetic points fitted to mocap data and then applied to real video data. Trajectories are coded with a general codebook learned from the same mocap data. NKTM is scalable to new action classes and training data as it does not require re-learning. Experiments on the IXMAS and N-UCLA datasets show that NKTM outperforms existing state-of-the-art methods for cross-view action recognition.
Random Tree Walk Toward Instantaneous 3D Human Pose Estimation
Authors: Ho Yub Jung, Soochahn Lee, Yong Seok Heo, Il Dong Yun
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Human Pose Estimation, CPU, Pose Estimation, Position, regression

The availability of accurate depth cameras have made real-time human pose estimation possible; however, there are still demands for faster algorithms on low power processors. This paper introduces 1000 frames per second pose estimation method on a single core CPU. A large computation gain is achieved by random walk sub-sampling. Instead of training trees for pixel-wise classification, a regression tree is trained to estimate the probability distribution to the direction toward the particular joint, relative to the current position. At test time, the direction for the random walk is randomly chosen from a set of representative directions. The new position is found by a constant step toward the direction, and the distribution for next direction is found at the new position. The continual random walk through 3D space will eventually produce an expectation of step positions, which we estimate as the joint position. A regression tree is built separately for each joint. The number of random walk steps can be assigned for each joint so that the computation time is consistent regardless of the size of body segmentation. The experiments show that even with large computation gain, the accuracy is higher or comparable to the state-of-the-art pose estimation methods.
Deep Hashing for Compact Binary Codes Learning
Authors: Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, Jie zhou
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deep Hashing

In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search. Unlike most existing binary codes learning methods which seek a single linear projection to map each sample into a binary vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the deep network: 1) the loss between the original real-valued feature descriptor and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) by including one discriminative term into the objective function of DH which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes. Experimental results show the superiority of the proposed approach over the state-of-the-arts.
Completing 3D Object Shape From One Depth Image
Authors: Jason Rock, Tanmay Gupta, Justin Thorsen, JunYoung Gwak, Daeyun Shin, Derek Hoiem
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object

Our goal is to recover a complete 3D model from a depth image of an object. Existing approaches rely on user interaction or apply to a limited class of objects, such as chairs. We aim to fully automatically reconstruct a 3D model from any category. We take an exemplar-based approach: retrieve similar objects in a database of 3D models using view-based matching and transfer the symmetries and surfaces from retrieved models. We investigate completion of 3D models in three cases: novel view (model in database); novel model (models for other objects of the same category in database); and novel category (no models from the category in database).
Encoding Based Saliency Detection for Videos and Images
Authors: Thomas Mauthner, Horst Possegger, Georg Waltner, Horst Bischof
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Detection, Activity Detection, Activity Recognition, Human Activity Recognition, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Video Saliency Detection

 We present a novel video saliency detection method to support human activity recognition and weakly supervised training of activity detection algorithms. Recent research has emphasized the need for analyzing salient information in videos to minimize dataset bias or to supervise weakly labeled training of activity detectors. In contrast to previous methods we do not rely on training information given by either eye-gaze or annotation data, but propose a fully unsupervised algorithm to find salient regions within videos. In general, we enforce the Gestalt principle of figure-ground segregation for both appearance and motion cues. We introduce an encoding approach that allows for efficient computation of saliency by approximating joint feature distributions. We evaluate our approach on several datasets, including challenging scenarios with cluttered background and camera motion, as well as salient object detection in images. Overall, we demonstrate favorable performance compared to state-of-the-art methods in estimating both ground-truth eye-gaze and activity annotations.
Online Sketching Hashing
Authors: Cong Leng, Jiaxiang Wu, Jian Cheng, Xiao Bai, Hanqing Lu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention. Extensive new algorithms have been developed and successfully applied to different applications. However, two critical problems are rarely mentioned. First, in real-world applications, the data often comes in a streaming fashion but most of existing hashing methods are batch based models. Second, when the dataset becomes huge, it is almost impossible to load all the data into memory to train hashing models. In this paper, we propose a novel approach to handle these two problems simultaneously based on the idea of data sketching. A sketch of one dataset preserves its major characters but with significantly smaller size. With a small size sketch, our method can learn hash functions in an online fashion, while needs rather low computational complexity and storage space. Extensive experiments on two large scale benchmarks and one synthetic dataset demonstrate the efficacy of the proposed method.
Enriching Object Detection With 2D-3D Registration and Continuous Viewpoint Estimation
Authors: Christopher Bongsoo Choy, Michael Stark, Sam Corbett-Davies, Silvio Savarese
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: GPU, object-detection, Object Detection, Pose Estimation, Viewpoint Estimation

A large body of recent work on object detection has focused on exploiting 3D CAD model databases to improve detection performance. Many of these approaches work by aligning exact 3D models to images using templates generated from renderings of the 3D models at a set of discrete viewpoints. However, the training procedures for these approaches are computationally expensive and require gigabytes of memory and storage, while the viewpoint discretization hampers pose estimation performance.  We propose an efficient method for synthesizing templates from 3D models that runs on the fly -- that is, it quickly produces detectors for an arbitrary viewpoint of a 3D model without expensive dataset-dependent training or template storage. Given a 3D model and an arbitrary continuous detection viewpoint, our method synthesizes a discriminative template by extracting features from a rendered view of the object and decorrelating spatial dependences among the features. Our decorrelation procedure relies on a gradient-based algorithm that is more numerically stable than standard decomposition-based procedures, and we efficiently search for candidate detections by computing FFT-based template convolutions. Due to the speed of our template synthesis procedure, we are able to perform joint optimization of scale, translation, continuous rotation, and focal length using Metropolis-Hastings algorithm. We provide an efficient GPU implementation of our algorithm, and we validate its performance on 3D Object Classes and PASCAL3D+ datasets.
Representing 3D Texture on Mesh Manifolds for Retrieval and Recognition Applications
Authors: Naoufel Werghi, Claudio Tortorici, Stefano Berretti, Alberto del Bimbo
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Recognition, Retrieval

In this paper, we present and experiment a novel approach for representing texture of 3D mesh manifolds using local binary patterns (LBP). Using a recently proposed framework [37], we compute LBP directly on the mesh surface, either using geometric or photometric appearance. Compared to its depth-image counterpart, our approach is distinguished by the following features: a) inherits the intrinsic advantages of mesh surface (e.g., preservation of the full geometry); b) does not require normalization; c) can accommodate partial matching. In addition, it allows early-level fusion of the geometry and photometric texture modalities. Through experiments conducted on two application scenarios, namely, 3D texture retrieval and 3D face recognition, we assess the effectiveness of the proposed solution with respect to state of the art approaches.
Saliency Propagation From Simple to Difficult
Authors: Chen Gong, DaCheng Tao, Wei Liu, Stephen J. Maybank, Meng Fang, Keren Fu, Jie Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Saliency Detection

Saliency propagation has been widely adopted for identifying the most attractive object in an image. The propagation sequence generated by existing saliency detection methods is governed by the spatial relationships of image regions, i.e., the saliency value is transmitted between two adjacent regions. However, for the inhomogeneous difficult adjacent regions, such a sequence may incur wrong propagations. In this paper, we attempt to manipulate the propagation sequence for optimizing the propagation quality. Intuitively, we postpone the propagations to difficult regions and meanwhile advance the propagations to less ambiguous simple regions. Inspired by the theoretical results in educational psychology, a novel propagation algorithm employing the teaching-to-learn and learning-to-teach strategies is proposed to explicitly improve the propagation quality. In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner. In the learning-to-teach step, the learner delivers its learning confidence to the teacher to assist the teacher to choose the subsequent simple regions. Due to the interactions between the teacher and learner, the uncertainty of original difficult regions is gradually reduced, yielding manifest salient objects with optimized background suppression. Extensive experimental results on benchmark saliency datasets demonstrate the superiority of the proposed algorithm over twelve representative saliency detectors.
Learning an Efficient Model of Hand Shape Variation From Depth Images
Authors: Sameh Khamis, Jonathan Taylor, Jamie Shotton, Cem Keskin, Shahram Izadi, Andrew Fitzgibbon
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We describe how to learn a compact and efficient model of the surface deformation of human hands. The model is built from a set of noisy depth images of a diverse set of subjects performing different poses with their hands. We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model. The model simultaneously accounts for variation in subject-specific shape and subject-agnostic pose. Specifically, hand shape is parameterized as a linear combination of a mean mesh in a neutral pose with a small number of offset vectors. This mesh is then articulated using standard linear blend skinning (LBS) to generate the control mesh of a subdivision surface. We define an energy that encourages each depth pixel to be explained by our model, and the use of a smooth subdivision surface allows us to optimize for all parameters jointly from a rough initialization. The efficacy of our method is demonstrated using both synthetic and real data, where it is shown that hand shape variation can be represented using only a small number of basis directions. We compare with other approaches including PCA and show a substantial improvement in the representation power of our model, while maintaining the efficiency of a linear shape basis.
On the Minimal Problems of Low-Rank Matrix Factorization
Authors: Fangyuan Jiang, Magnus Oskarsson, Kalle Astrom
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Low-rank matrix factorization is an essential problem in many areas including computer vision, with applications in e.g.  affine structure-from-motion, photometric stereo, and non-rigid structure from motion. However, very little attention  has been drawn to minimal cases for this problem or to using the minimal configuration of observations to find the solution. Minimal problems are useful when either outliers are present or  the observation matrix is sparse. In this paper, we first give some theoretical insights on how to generate all the minimal problems of a given size using Laman graph theory. We then propose a new parametrization and a building-block scheme to solve these minimal problems by extending the solution from a small sized minimal problem. We test our solvers on synthetic data as well as real data with outliers or a large portion of missing data and show that our method can handle the cases when other iterative methods, based on convex relaxation, fail.
Symmetry-Based Text Line Detection in Natural Scenes
Authors: Zheng Zhang, Wei Shen, Cong Yao, Xiang Bai
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Line Detection, Scene Text Detection, Text Detection

Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes. Therefore, scene text detection and recognition have become active research topics in computer vision. In this work, we investigate the problem of scene text detection from an alternative perspective and propose a novel algorithm for it. Different from traditional methods, which mainly make use of the properties of single characters or strokes, the proposed algorithm exploits the symmetry property of character groups and allows for direct extraction of text lines from natural images. The experiments on the latest ICDAR benchmarks demonstrate that the proposed algorithm achieves state-of-the-art performance. Moreover, compared to conventional approaches, the proposed algorithm shows stronger adaptability to texts in challenging scenarios.
DevNet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting
Authors: Chuang Gan, Naiyan Wang, Yi Yang, Dit-yan Yeung, Alex G. Hauptmann
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Event Detection, image-classification, Image Classification, Temporal Action Localization

In this paper, we focus on complex event detection in internet videos while also providing the key evidences of the detection results. Convolutional Neural Networks (CNNs) have achieved promising performance in image classification and action recognition tasks. However, it remains an open problem how to use CNNs for video event detection and recounting, mainly due to the complexity and diversity of video events. In this work, we propose a flexible deep CNN infrastructure, namely Deep Event Network (DevNet), that simultaneously detects pre-defined events and provides key spatial temporal evidences. Taking key frames of videos as input, we first detect the event of interest at the video level by aggregating the CNN features of the key frames. The pieces of evidences which recount the detection results, are also automatically localized, both temporally and spatially. The challenge is that we only have video level labels, while the key evidences usually take place at the frame levels. Based on the intrinsic property of CNNs, we first generate a spatial-temporal saliency map by back passing through DevNet, which then can be used to find the key frames which are most indicative to the event, as well as to localize the specific spatial position, usually an object, in the frame of the highly indicative area. Experiments on the large scale TRECVID 2014 MEDTest dataset demonstrate the promising performance of our method, both for event detection and evidence recounting.
Learning to Detect Motion Boundaries
Authors: Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, Cordelia Schmid
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Boundary Detection, Optical Flow Estimation

We propose a learning-based approach for motion boundary detection. Precise localization of motion boundaries is essential for the success of optical flow estimation, as motion boundaries correspond to discontinuities of the optical flow field. The proposed approach allows to predict motion boundaries, using a structured random forest trained on the ground-truth of the MPI-Sintel dataset. The random forest leverages several cues at the patch level, namely appearance (RGB color) and motion cues (optical flow estimated by state-of-the-art algorithms). Experimental results show that the proposed approach is both robust and computationally efficient. It significantly outperforms state-of-the-art motion-difference approaches on the MPI-Sintel and Middlebury datasets. We compare the results obtained with several state-of-the-art optical flow approaches and study the impact of the different cues used in the random forest.Furthermore, we introduce a new dataset, the YouTube Motion Boundaries dataset (YMB), that comprises 60 sequences taken from real-world videos with manually annotated motion boundaries. On this dataset, our approach, although trained on MPI-Sintel, also outperforms by a large margin state-of-the-art optical flow algorithms.
Improving Object Proposals With Multi-Thresholding Straddling Expansion
Authors: Xiaozhi Chen, Huimin Ma, Xiang Wang, Zhichen Zhao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection

Recent advances in object detection have exploited object proposals to speed up object searching. However, many of existing object proposal generators have strong localization bias or require computationally expensive diversification strategies. In this paper, we present an effective approach to address these issues. We first propose a simple and useful localization bias measure, called superpixel tightness. Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification. Our method is essentially a box refinement process, which is intuitive and beneficial, but seldom exploited before. The greatest benefit of our method is that it can be integrated into any existing model to achieve consistently high recall across various intersection over union thresholds. Experiments on PASCAL VOC dataset demonstrates that our approach improves numerous existing models significantly with little computational overhead.