pwc-archive/papers-with-abstracts huggingface dataset:
Pairwise Matching Through Max-Weight Bipartite Belief Propagation
Authors: Zhen Zhang, Qinfeng Shi, Julian McAuley, Wei Wei, Yanning Zhang, Anton Van Den Hengel
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

Feature matching is a key problem in computer vision and pattern recognition. One way to encode the essential interdependence between potential feature matches is to cast the problem as inference in a graphical model, though recently alternatives such as spectral methods, or approaches based on the convex-concave procedure have achieved the state-of-the-art. Here we revisit the use of graphical models for feature matching, and propose a belief propagation scheme which exhibits the following advantages: (1) we explicitly enforce one-to-one matching constraints; (2) we offer a tighter relaxation of the original cost function than previous graphical-model-based approaches; and (3) our sub-problems decompose into max-weight bipartite matching, which can be solved efficiently, leading to orders-of-magnitude reductions in execution time. Experimental results show that the proposed algorithm produces results superior to those of the current state-of-the-art.
Structured Feature Similarity With Explicit Feature Map
Authors: Takumi Kobayashi
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Form, Image Quality Assessment, SSIM

Feature matching is a fundamental process in a variety of computer vision tasks. Beyond the standard L2 metric, various methods to measure similarity between features have been proposed mainly on the assumption that the features are defined in a histogram form. On the other hand, in a field of image quality assessment, SSIM produces effective similarity between images, taking the place of L2 metric. In this paper, we propose a feature similarity measurement method based on the SSIM. Unlike the previous methods, the proposed method is built on not a histogram form but a tensor structure of a feature array extracted such as on spatial grids, in order to construct effective SSIM-based similarity measure of high robustness which is a key requirement in feature matching. In addition, we provide the explicit feature map such that the proposed similarity metric is embedded as a dot product. It contributes to significant speedup in similarity measurement as well as to feature transformation toward an effective vector form to which linear classifiers are directly applicable. In the experiments on various tasks, the proposed method exhibits favorable performance in both feature matching and classification.
Temporal Epipolar Regions
Authors: Mor Dar, Yael Moses
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: valid

Dynamic events are often photographed by a number of people from different viewpoints at different times, resulting in an unconstrained set of images. Finding the corresponding moving features in each of the images allows us to extract information about objects of interest in the scene. Computing correspondence of moving features in such a set of images is considerably more challenging than computing correspondence in video due to possible significant differences in viewpoints and inconsistent timing between image captures. The prediction methods used in video for improving robustness and efficiency are not applicable to a set of still images. In this paper we propose a novel method to predict locations of an approximately linear moving feature point, given a small subset of correspondences and the temporal order of image captures. Our method extends the use of epipolar geometry to divide images into valid and invalid regions, termed Temporal Epipolar Regions (TERs). We formally prove that the location of a feature in a new image is restricted to valid TERs. We demonstrate the effectiveness of our method in reducing the search space for correspondence on both synthetic and challenging real world data, and show the improved matching.
Similarity Learning With Spatial Constraints for Person Re-Identification
Authors: Dapeng Chen, Zejian yuan, Badong Chen, Nanning Zheng
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Re-Identification

Pose variation remains one of the major factors that adversely affect the accuracy of person re-identification. Such variation is not arbitrary as body parts (e.g. head, torso, legs) have relative stable spatial distribution. Breaking down the variability of global appearance regarding the spatial distribution potentially benefits the person matching. We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion. In particular, we take advantage of the recently proposed polynomial feature map to describe the matching within each subregion, and inject all the feature maps into a unified framework. The framework not only  outputs similarity measurements for different regions, but also makes a better consistency among them. Our framework can collaborate local similarities as well as global similarity to exploit their complementary strength. It is flexible to incorporate multiple visual cues to further elevate the performance. In experiments, we analyze the effectiveness of the major components. The results on four datasets show significant and consistent improvements over the state-of-the-art methods.
Sample-Specific SVM Learning for Person Re-Identification
Authors: Ying Zhang, Baohua Li, Huchuan Lu, Atshushi Irie, Xiang Ruan
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Dictionary Learning, imbalanced classification, Person Re-Identification

Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics. However, most existing approaches focus on learning a fixed distance metric for all instance pairs, while ignoring the individuality of each person. In this paper, we formulate the person re-identification problem as an imbalanced classification problem and learn a classifier specifically for each pedestrian such that the matching model is highly tuned to the individual's appearance. To establish correspondence between feature space and classifier space, we propose a Least Square Semi-Coupled Dictionary Learning (LSSCDL) algorithm to learn a pair of dictionaries and a mapping function efficiently. Extensive experiments on a series of challenging databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches, especially on the rank-1 recognition rate.
Joint Learning of Single-Image and Cross-Image Representations for Person Re-Identification
Authors: Faqiang Wang, WangMeng Zuo, Liang Lin, David Zhang, Lei Zhang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Re-Identification, Triplet

Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning framework to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.
A Multi-Level Contextual Model For Person Recognition in Photo Albums
Authors: Haoxiang Li, Jonathan Brandt, Zhe Lin, Xiaohui Shen, Gang Hua
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Recognition

In this work, we present a new framework for person recognition in photo albums that exploits contextual cues at multiple levels, spanning individual persons, individual photos, and photo groups.  Through experiments, we show that the information available at each of these distinct contextual levels provides complementary cues as to person identities.  At the person level, we leverage clothing and body appearance in addition to facial appearance, and to compensate for instances where the faces are not visible.  At the photo level we leverage a learned prior on the joint distribution of identities on the same photo to guide the identity assignments. Going beyond a single photo, we are able to infer natural groupings of photos with shared context in an unsupervised manner. By exploiting this shared contextual information, we are able to reduce the identity search space and exploit higher intra-personal appearance consistency within photo groups. Our new framework enables efficient use of these complementary multi-level contextual cues to improve overall recognition rates on the photo album person recognition task, as demonstrated through state-of-the-art results on a challenging public dataset.  Our results outperform competing methods by a significant margin, while being computationally efficient and practical in a real world application.
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification
Authors: Peixi Peng, Tao Xiang, Yao-Wei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Dictionary Learning, Person Re-Identification, Transfer Learning

Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in real-world applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative representation. It is unsupervised in the sense that the target dataset is completely unlabelled. Specifically, we present an multi-task dictionary learning method which is able to learn a dataset-shared but target-data-biased representation. Experimental results on five benchmark datasets demonstrate that the method significantly outperforms the state-of-the-art.
Recurrent Convolutional Network for Video-Based Person Re-Identification
Authors: Niall McLaughlin, Jesus Martinez del Rincon, Paul Miller
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Optical Flow Estimation, Person Re-Identification, Video-Based Person Re-Identification

In this paper we propose a novel recurrent neural network architecture for video-based person re-identification. Given the video sequence of a person, features are extracted from each frame using a convolutional neural network that incorporates a recurrent final layer, which allows information to flow between time-steps. The features from all time-steps are then combined using temporal pooling to give an overall appearance feature for the complete sequence. The convolutional network, recurrent layer, and temporal pooling layer, are jointly trained to act as a feature extractor for video-based re-identification using a Siamese network architecture. Our approach makes use of colour and optical flow information in order to capture appearance and motion information which is useful for video re-identification. Experiments are conduced on the iLIDS-VID and PRID-2011 datasets to show that this approach outperforms existing methods of video-based re-identification.
Person Re-Identification by Multi-Channel Parts-Based CNN With Improved Triplet Loss Function
Authors: De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, Nanning Zheng
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Re-Identification, Robust Face Recognition, Triplet

Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras. In this paper, we present a novel multi-channel parts-based convolutional neural network (CNN) model under the triplet framework for person re-identification. Specifically, the proposed CNN model consists of multiple channels to jointly learn both the global full body and local body-parts features of the input persons. The CNN model is trained by an improved triplet loss function that serves to pull the instances of the same person closer, and at the same time push the instances belonging to different persons farther from each other in the learned feature space. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based ones, on the challenging i-LIDS, VIPeR, PRID2011 and CUHK01 datasets.
Improving Person Re-Identification via Pose-Aware Multi-Shot Matching
Authors: Yeong-Jun Cho, Kuk-Jin Yoon
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Re-Identification

Person re-identification is the problem of recognizing people across images or videos from non-overlapping views. Although there has been much progress in person re-identification for the last decade, it still remains a challenging task because of severe appearance changes of a person due to diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person re-identification by analyzing camera viewpoints and person poses, so-called Pose-aware Multi-shot Matching (PaMM), which robustly estimates target poses and efficiently conducts multi-shot matching based on the target pose information. Experimental results using public person re-identification dataset show that the proposed methods are promising for person re-identification under diverse viewpoints and pose variances.
Hierarchical Gaussian Descriptor for Person Re-Identification
Authors: Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: image-classification, Image Classification, Person Re-Identification

Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification. In this paper, we present a novel descriptor based on a hierarchical distribution of pixel features. A hierarchical covariance descriptor has been successfully applied for image classification. However, the mean information of pixel features, which is absent in covariance, tends to be major discriminative information of person images. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, we model the region as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, unlike the hierarchical covariance descriptor, the proposed descriptor can model both the mean and the covariance information of pixel features properly. The results of experiments conducted on five databases indicate that the proposed descriptor exhibits remarkably high performance which outperforms the state-of-the-art descriptors for person re-identification.
STCT: Sequentially Training Convolutional Networks for Visual Tracking
Authors: Lijun Wang, Wanli Ouyang, Xiaogang Wang, Huchuan Lu
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Visual Tracking

Due to the limited amount of training samples, fine-tuning pre-trained deep models online is prone to over-fitting. In this paper, we propose a sequential training method for convolutional neural networks (CNNs) to effectively transfer pre-trained deep features for online applications. We regard a CNN as an ensemble with each channel of the output feature map as an individual base learner. Each base learner is trained using different loss criterions to reduce correlation and avoid over-training. To achieve the best ensemble online, all the base learners are sequentially sampled into the ensemble via important sampling. To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features.  The proposed online training method is applied to visual tracking problem by transferring deep features trained on massive annotated visual data and is shown to significantly improve tracking performance. Extensive experiments are conducted on two challenging benchmark data set and demonstrate that our tracking algorithm can outperform state-of-the-art methods with a considerable margin.
Determining Occlusions From Space and Time Image Reconstructions
Authors: Juan-Manuel Perez-Rua, Tomas Crivelli, Patrick Bouthemy, Patrick Perez
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

The problem of localizing occlusions between consecutive frames of a video is important but rarely tackled on its own. In most works, it is tightly interleaved with the computation of accurate optical flows, which leads to a delicate chicken-and-egg problem. With this in mind, we propose a novel approach to occlusion detection where visibility or not of a point in next frame is formulated in terms of visual reconstruction. The key issue is now to determine how well a pixel in the first image can be "recon- structed" from co-located colors in the next image. We first exploit this reasoning at the pixel level with a new detection criterion. Contrary to the ubiquitous displaced-frame-difference and forward-backward flow vector matching, the proposed alternative does not critically depend on a precomputed, dense displacement field, while being shown to be more effective. We then leverage this local modeling within an energy-minimization framework that delivers occlusion maps. An easy-to-obtain collection of parametric motion models is exploited within the energy to provide the required level of motion information. Our approach outperforms state-of-the-art detection methods on the challenging MPI Sintel dataset.
Online Multi-Object Tracking via Structural Constraint Event Aggregation
Authors: Ju Hong Yoon, Chang-Ryeol Lee, Ming-Hsuan Yang, Kuk-Jin Yoon
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking, Online Multi-Object Tracking

Multi-object tracking (MOT) becomes more challenging when objects of interest have similar appearances. In that case, the motion cues are particularly useful for discriminating multiple objects. However, for online 2D MOT in scenes acquired from moving cameras, observable motion cues are complicated by global camera movements and thus not always smooth or predictable. To deal with such unexpected camera motion for online 2D MOT, a structural motion constraint between objects has been utilized thanks to its robustness to camera motion. In this paper, we propose a new data association method that effectively exploits structural motion constraints in the presence of large camera motion. In addition, to further improve the robustness of data association against mis-detections and clutters, a novel event aggregation approach is developed to integrate structural constraints in assignment costs for online MOT. Experimental results on a large number of datasets demonstrate the effectiveness of the proposed algorithm for online 2D MOT.
3D Part-Based Sparse Tracker With Automatic Synchronization and Registration
Authors: Adel Bibi, Tianzhu Zhang, Bernard Ghanem
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Occlusion Handling

In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused  in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers.
Recurrently Target-Attending Tracking
Authors: Zhen Cui, Shengtao Xiao, Jiashi Feng, Shuicheng Yan
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Visual Tracking

Robust visual tracking is a challenging task in computer vision. Due to the accumulation and propagation of estimation error, model drifting often occurs and degrades the tracking performance. To mitigate this problem, in this paper we propose a novel tracking method called Recurrently Target-attending Tracking (RTT). RTT attempts to identify and exploit those reliable parts which are beneficial for the overall tracking process. To bypass occlusion and discover reliable components, multi-directional Recurrent Neural Networks (RNNs) are employed in RTT to capture long-range contextual cues by traversing a candidate spatial region from multiple directions. The produced confidence maps from the RNNs are employed to adaptively regularize the learning of discriminative correlation filters by suppressing clutter background noises while making full use of the information from reliable parts. To solve the weighted correlation filters, we especially derive an efficient closed-form solution with a sharp reduction in computation complexity. Extensive experiments demonstrate that our proposed RTT is more competitive over those correlation filter based methods.
Structured Regression Gradient Boosting
Authors: Ferran Diego, Fred A. Hamprecht
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Depth Estimation, Image Inpainting, regression, Vessel Detection

We propose a new way to train a structured output prediction model. More specifically, we train  nonlinear data terms in a Gaussian Conditional Random Field (GCRF) by a generalized version of gradient boosting. The approach is evaluated on three challenging regression benchmarks: vessel detection, single image depth estimation and image inpainting. These experiments suggest that the proposed boosting framework matches or exceeds the state-of-the-art.