pwc-archive/papers-with-abstracts huggingface dataset:
Point to Set Similarity Based Deep Feature Learning for Person Re-Identification
Authors: Sanping Zhou, Jinjun Wang, Jiayun Wang, Yihong Gong, Nanning Zheng
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Re-Identification

Person re-identification (Re-ID) remains a challenging problem due to significant appearance changes caused by variations in  view angle, background clutter, illumination condition and mutual occlusion. To address these issues, conventional methods usually focus on proposing robust feature representation or learning metric transformation based on pairwise similarity, using Fisher-type criterion. The recent development in deep learning based approaches address the two processes in a joint fashion and have achieved promising progress. One of the key issues for deep learning based person Re-ID is the selection of proper similarity comparison criteria, and the performance of learned features using existing criterion based on pairwise similarity is still limited, because only P2P distances are mostly considered. In this paper, we present a novel person Re-ID method based on P2S similarity comparison. The P2S metric can jointly minimize the intra-class distance and maximize the inter-class distance, while back-propagating the gradient to optimize parameters of the deep model. By utilizing our proposed P2S metric, the learned deep model can effectively distinguish different persons by learning discriminative and stable feature representations. Comprehensive experimental evaluations on 3DPeS, CUHK01, PRID2011 and Market1501 datasets demonstrate the advantages of our method over the state-of-the-art approaches.
Gated Feedback Refinement Network for Dense Image Labeling
Authors: Md Amirul Islam, Mrigank Rochan, Neil D. B. Bruce, Yang Wang
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder

Effective integration of local and global contextual information is crucial for dense labeling problems. Most existing methods based on an encoder-decoder architecture simply concatenate features from earlier layers to obtain higher-frequency details in the refinement stages. However, there are limits to the quality of refinement possible if ambiguous information is passed forward. In this paper we propose Gated Feedback Refinement Network (G-FRNet), an end-to-end deep learning framework for dense labeling tasks that addresses this limitation of existing methods. Initially, G-FRNet makes a coarse prediction and then it progressively refines the details by efficiently integrating local and global contextual information during the refinement stages. We introduce gate units that control the information passed forward in order to filter out ambiguity. Experiments on three challenging dense labeling datasets (CamVid, PASCAL VOC 2012, and Horse-Cow Parsing) show the effectiveness of our method. Our proposed approach achieves state-of-the-art results on the CamVid and Horse-Cow Parsing datasets, and produces competitive results on the PASCAL VOC 2012 dataset.
Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders
Authors: Xin Yu, Fatih Porikli
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder, Face Hallucination, Hallucination

Most of the conventional face hallucination methods assume the input image is sufficiently large and aligned, and all require the input image to be noise-free. Their performance degrades drastically if the input image is tiny, unaligned, and contaminated by noise.   In this paper, we introduce a novel transformative discriminative autoencoder to 8X super-resolve unaligned noisy and tiny (16X16) low-resolution face images. In contrast to encoder-decoder based autoencoders, our method uses decoder-encoder-decoder networks. We first employ a transformative discriminative decoder network to upsample and denoise simultaneously. Then we use a transformative encoder network to project the intermediate HR faces to aligned and noise-free LR faces. Finally, we use the second decoder to generate hallucinated HR images. Our extensive evaluations on a very large face dataset show that our method achieves superior hallucination results and outperforms the state-of-the-art by a large margin of 1.82dB PSNR.
Learning Dynamic Guidance for Depth Image Enhancement
Authors: Shuhang Gu, WangMeng Zuo, Shi Guo, Yunjin Chen, Chongyu Chen, Lei Zhang
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Depth Image Upsampling, Image Enhancement, Image Restoration

The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizing the complex and dynamic dependency between intensity and depth images. To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance. First, we generalize the analysis representation model by including a guided weight function for dependency modeling. And the task-driven learning formulation is introduced to obtain the optimized guidance tailored to specific enhancement task. Second, the depth image is gradually enhanced along with the iterations, and thus the guidance should also be dynamically adjusted  to account for the updating of depth image. To this end, stage-wise parameters are learned for dynamic guidance. Experiments on guided depth image upsampling and noisy depth image restoration validate the effectiveness of our method.
Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning
Authors: Xing Xu, Fumin Shen, Yang Yang, Dongxiang Zhang, Heng Tao Shen, Jingkuan Song
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Retrieval, Transfer Learning, Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize objects of unseen classes with available training data from another set of seen classes. Existing solutions are focused on exploring knowledge transfer via an intermediate semantic embedding (e.g.s, attributes) shared between seen and unseen classes. In this paper, we propose a novel projection framework based on matrix tri-factorization with manifold regularizations. Specifically, we learn the semantic embedding projection by decomposing the visual feature matrix under the guidance of semantic embedding and class label matrices. By additionally introducing manifold regularizations on visual data and semantic embeddings, the learned projection can effectively captures the geometrical manifold structure residing in both visual and semantic spaces. To avoid the projection domain shift problem, we devise an effective prediction scheme by exploiting the test-time manifold structure. Extensive experiments on four benchmark datasets show that our approach significantly outperforms the state-of-the-arts, yielding an average improvement  ratio by 7.4% and 31.9% for the recognition and retrieval task, respectively.
Spatio-Temporal Alignment of Non-Overlapping Sequences From Independently Panning Cameras
Authors: Seyed Morteza Safdarnejad, Xiaoming Liu
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

This paper addresses the problem of spatio-temporal alignment of multiple video sequences. We identify and tackle a novel scenario of this problem referred to as Nonoverlapping Sequences (NOS). NOS are captured by multiple freely panning handheld cameras whose field of views (FOV) might have no direct spatial overlap. With the popularity of mobile sensors, NOS rise when multiple cooperative users capture a public event to create a panoramic video, or when consolidating multiple footages of an incident into a single video. To tackle this novel scenario, we first spatially align the sequences by reconstructing the background of each sequence and registering these backgrounds, even if the backgrounds are not overlapping. Given the spatial alignment, we temporally synchronize the sequences, such that the trajectories of moving objects (e.g., cars or pedestrians) are consistent across sequences. Experimental results demonstrate the performance of our algorithm in this novel and challenging scenario, quantitatively and qualitatively.
Distinguishing the Indistinguishable: Exploring Structural Ambiguities via Geodesic Context
Authors: Qingan Yan, Long Yang, Ling Zhang, Chunxia Xiao
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

A perennial problem in structure from motion (SfM) is visual ambiguity posed by repetitive structures. Recent disambiguating algorithms infer ambiguities mainly via explicit background context, thus face limitations in highly ambiguous scenes which are visually indistinguishable. Instead of analyzing local visual information, we propose a novel algorithm for SfM disambiguation that explores the global topology as encoded in photo collections. An important adaptation of this work is to approximate the available imagery using a manifold of viewpoints. We note that, while ambiguous images appear deceptively similar in appearance, they are actually located far apart on geodesics. We establish the manifold by adaptively identifying cameras with adjacent viewpoint, and detect ambiguities via a new measure, geodesic consistency. We demonstrate the accuracy and efficiency of the proposed approach on a range of complex ambiguity datasets, even including the challenging scenes without background conflicts.
Removing Rain From Single Images via a Deep Detail Network
Authors: Xueyang Fu, Jia-Bin Huang, Delu Zeng, Yue Huang, Xinghao Ding, John Paisley
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Denoising, Rain Removal

We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and focuses the model on the structure of rain in images. This demonstrates that a deep architecture not only has benefits for high-level vision tasks but also can be used to solve low-level imaging problems. Though we train the network on synthetic data, we find that the learned network generalizes well to real-world test images. Experiments show that the proposed method significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. We discuss applications of this structure to denoising and JPEG artifact reduction at the end of the paper.
Binarized Mode Seeking for Scalable Visual Pattern Discovery
Authors: Wei Zhang, Xiaochun Cao, Rui Wang, Yuanfang Guo, Zhineng Chen
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

This paper studies visual pattern discovery in large-scale image collections via binarized mode seeking, where images can only be represented as binary codes for efficient storage and computation. We address this problem from the perspective of binary space mode seeking. First, a binary mean shift (bMS) is proposed to discover frequent patterns via mode seeking directly in binary space. The binomial-based kernel and binary constraint are introduced for binarized analysis. Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset. With the binarized algorithm and optimization, our methods demonstrate significant computation (50X) and storage (32X) improvement compared to standard techniques operating in Euclidean space, while the performance does not largely degenerate. Furthermore, cbMS discovers more informative patterns by suppressing low discriminative modes. We evaluate our methods on both annotated ILSVRC (1M images) and un-annotated blind Flickr (10M images) datasets with million scale images, which demonstrates both the scalability and effectiveness of our algorithms for discovering frequent and informative patterns in large scale collection.
Fast Person Re-Identification via Cross-Camera Semantic Binary Transformation
Authors: Jiaxin Chen, Yunhong Wang, Jie Qin, Li Liu, Ling Shao
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Re-Identification

Numerous methods have been proposed for person re-identification, most of which however neglect the matching efficiency. Recently, several hashing based approaches have been developed to make re-identification more scalable for large-scale gallery sets. Despite their efficiency, these works ignore cross-camera variations, which severely deteriorate the final matching accuracy. To address the above issues, we propose a novel hashing based method for fast person re-identification, namely Cross-camera Semantic Binary Transformation (CSBT). CSBT aims to transform original high-dimensional feature vectors into compact identity-preserving binary codes. To this end, CSBT first employs a subspace projection to mitigate cross-camera variations, by maximizing intra-person similarities and inter-person discrepancies. Subsequently, a binary coding scheme is proposed via seamlessly incorporating both the semantic pairwise relationships and local affinity information. Finally, a joint learning framework is proposed for simultaneous subspace projection learning and binary coding based on discrete alternating optimization. Experimental results on four benchmarks clearly demonstrate the superiority of CSBT over the state-of-the-art methods.
Deep Crisp Boundaries
Authors: Yupei Wang, Xin Zhao, Kaiqi Huang
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Edge Detection, Optical Flow Estimation

Edge detection had made significant progress with the help of deep Convolutional Networks (ConvNet). ConvNet based edge detectors approached human level performance on standard benchmarks. We provide a systematical study of these detector outputs, and show that they failed to accurately localize edges, which can be adversarial for tasks that require crisp edge inputs. In addition, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve promising performance on BSDS500, surpassing human accuracy when using standard criteria, and largely outperforming state-of-the-art methods when using more strict criteria. We further demonstrate the benefit of crisp edge maps for estimating optical flow and generating object proposals.
Learning Multifunctional Binary Codes for Both Category and Attribute Oriented Retrieval Tasks
Authors: Haomiao Liu, Ruiping Wang, Shiguang Shan, Xilin Chen
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Attribute, Image Retrieval, Retrieval

In this paper we propose a unified framework to address multiple realistic image retrieval tasks concerning both category and attributes. Considering the scale of modern datasets, hashing is favorable for its low complexity. However, most existing hashing methods are designed to preserve one single kind of similarity, thus incapable of dealing with the different tasks simultaneously. To overcome this limitation, we propose a new hashing method, named Dual Purpose Hashing (DPH), which jointly preserves the category and attribute similarities by exploiting the convolutional networks (CNN) to hierarchically capture the correlations between category and attributes. Since images with both category and attribute labels are scarce, our method is designed to take the abundant partially labelled images on the Internet as training inputs. With such a framework, the binary codes of new-coming images can be readily obtained by quantizing the network outputs of a binary-like layer, and the attributes can be recovered from the codes easily. Experiments on two large-scale datasets show that our dual purpose hash codes can achieve comparable or even better performance than those state-of-the-art methods specifically designed for each individual retrieval task, while being more compact than the compared methods.
Wetness and Color From a Single Multispectral Image
Authors: Mihoko Shimano, Hiroki Okawa, Yuta Asano, Ryoma Bise, Ko Nishino, Imari Sato
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Autonomous Vehicles

Visual recognition of wet surfaces and their degrees of wetness is important for many computer vision applications. It can inform slippery spots on a road to autonomous vehicles, muddy areas of a trail to humanoid robots, and the freshness of groceries to us. In the past, monochromatic appearance change, the fact that surfaces darken when wet, has been modeled to recognize wet surfaces. In this paper, we show that color change, particularly in its spectral behavior, carries rich information about a wet surface. We derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening due to multiple scattering and absorption in the surface. We derive a novel method for estimating key parameters of this spectral appearance model, which enables the recovery of the original surface color and the degree of wetness from a single observation. Applied to a multispectral image, the method estimates the spatial map of wetness together with the dry spectral distribution of the surface. To our knowledge, this work is the first to model and leverage the spectral characteristics of wet surfaces to revert its appearance. We conduct comprehensive experimental validation with a number of wet real surfaces. The results demonstrate the accuracy of our model and the effectiveness of our method for surface wetness and color estimation.
Learning to Rank Retargeted Images
Authors: Yang Chen, Yong-Jin Liu, Yu-Kun Lai
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Retargeting, Learning-To-Rank

Image retargeting techniques that adjust images into different sizes have attracted much attention recently. Objective quality assessment (OQA) of image retargeting results is often desired to automatically select the best results. Existing OQA methods output an absolute score for each retargeted image and use these scores to compare different results. Observing that it is challenging even for human subjects to give consistent scores for retargeting results of different source images, in this paper we propose a learning-based OQA method that predicts the ranking of a set of retargeted images with the same source image. We show that this more manageable task helps achieve more consistent prediction to human preference and is sufficient for most application scenarios. To compute the ranking, we propose a simple yet efficient machine learning framework that uses a General Regression Neural Network (GRNN) to model a combination of seven elaborate OQA metrics. We then propose a simple scheme to transform the relative scores output from GRNN into a global ranking. We train our GRNN model using human preference data collected in the elaborate RetargetMe benchmark and evaluate our method based on the subjective study in RetargetMe. Moreover, we introduce a further subjective benchmark to evaluate the generalizability of different OQA methods. Experimental results demonstrate that our method outperforms eight representative OQA methods in ranking prediction and has better generalizability to different datasets.
Image Deblurring via Extreme Channels Prior
Authors: Yanyang Yan, Wenqi Ren, Yuanfang Guo, Rui Wang, Xiaochun Cao
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deblurring, Image Deblurring

Camera motion introduces motion blur, affecting many computer vision tasks. Dark Channel Prior (DCP) helps the blind deblurring on scenes including natural, face, text, and low-illumination images. However, it has limitations and is less likely to support the kernel estimation while bright pixels dominate the input image. We observe that the bright pixels in the clear images are not likely to be bright after the blur process. Based on this observation, we first illustrate this phenomenon mathematically and define it as the Bright Channel Prior (BCP). Then, we propose a technique for deblurring such images which elevates the performance of existing motion deblurring algorithms. The proposed method takes advantage of both Bright and Dark Channel Prior. This joint prior is named as extreme channels prior and is crucial for achieving efficient restorations by leveraging both the bright and dark information. Extensive experimental results demonstrate that the proposed method is more robust and performs favorably against the state-of-the-art image deblurring methods on both synthesized and natural images.
One-Shot Hyperspectral Imaging Using Faced Reflectors
Authors: Tsuyoshi Takatani, Takahito Aoto, Yasuhiro Mukaigawa
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Material Recognition

Hyperspectral imaging is a useful technique for various computer vision tasks such as material recognition. However, such technique usually requires an expensive and professional setup and is time-consuming because a conventional hyperspectral image consists of a large number of observations. In this paper, we propose a novel technique of one-shot hyperspectral imaging using faced reflectors on which color filters are attached. The key idea is based on the principle that each of multiple reflections on the filters has a different spectrum, which allows us to observe multiple intensities through different spectra. Our technique can be implemented either by a coupled mirror or a kaleidoscope geometry. Experimental results show that our technique is capable of accurately capturing a hyperspectral image by using a coupled mirror setup which is readily available.
A Novel Tensor-Based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors
Authors: Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, Yao Wang
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Dictionary Learning

Rain streaks removal is an important issue of the outdoor vision system and has been recently investigated extensively. In this paper, we propose a novel tensor based video rain streaks removal approach by fully considering the discriminatively intrinsic characteristics of rain streaks and clean videos, which needs neither rain detection nor time-consuming dictionary learning stage. In specific, on the one hand, rain streaks are sparse and smooth along the raindrops' direction, and on the other hand, the clean videos possess smoothness along the rain-perpendicular direction and global and local correlation along time direction. We use the l_1 norm to enhance the sparsity of the underlying rain, two unidirectional Total Variation (TV) regularizers to guarantee the different discriminative smoothness, and a tensor nuclear norm and a time directional difference operator to characterize the exclusive correlation of the clean video along time. Alternation direction method of multipliers (ADMM) is employed to solve the proposed concise tensor based convex model. Experiments implemented on synthetic and real data substantiate the effectiveness and efficiency of the proposed method. Under comprehensive quantitative performance measures, our approach outperforms other state-of-the-art methods.