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检索条件"任意字段=30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017"

共 212 条记录，以下是151-160 订阅

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Convolutional neural network architecture for geometric matching 30

Convolutional neural network architecture for geometric matc...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Rocco, Ignacio Arandjelovic, Relja Sivic, Josef DI ENS Paris France INRIA Villeneuve Dascq France CIIRC Prague Czech Republic PSL Res Univ ENS Dept Informat CNRS F-75005 Paris France Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic DeepMind London England

ISBN: (纸本)9781538604571

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. the contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. the architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous in-lier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging Proposal Flow dataset.

关键词： Correlation Feature extraction computer architecture Estimation Geometry Robustness Neural networks

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SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objects in Weakly Labelled Videos 30

SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objec...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Dingwen Yang, Le Meng, Deyu Xu, Dong Han, Junwei Northwestern Polytechincal Univ Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781538604571

Object segmentation in weakly labelled videos is an interesting yet challenging task, which aims at learning to perform category-specific video object segmentation by only using video-level tags. Existing works in this research area might still have some limitations, e.g., lack of effective DNN-based learning frameworks, under-exploring the context information, and requiring to leverage the unstable negative video collection, which prevent them from obtaining more promising performance. To this end, we propose a novel self-paced fine-tuning network (SPFTN)-based framework, which could learn to explore the context information within the video frames and capture adequate object semantics without using the negative videos. To perform weakly supervised learning based on the deep neural network, we make the earliest effort to integrate the self-paced learning regime and the deep neural network into a unified and compatible framework, leading to the self-paced fine-tuning network. Comprehensive experiments on the large-scale YouTube-Objects and DAVIS datasets demonstrate that the proposed approach achieves superior performance as compared with other state-of-the-art methods as well as the baseline networks and models.

关键词： Videos Training Object segmentation Image segmentation computer vision Neural networks Videotapes Object segmentation Neural network images computer vision image segmentation Functional training VIDEO FRAMES

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DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal 30

DeshadowNet: A Multi-context Embedding Deep Network for Shad...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qu, Liangqiong Tian, Jiandong He, Shengfeng Tang, Yandong Lau, Rynson W. H. Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China City Univ Hong Kong Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538604571

Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. the first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the other one involves semantic understanding for final prediction. these two complementary networks generate multi-context features to obtain the shadow matte with fine local details. To evaluate the performance of the proposed method, we construct the first large scale benchmark with 3088 image pairs. Extensive experiments on two publicly available benchmarks and our large-scale benchmark show that the proposed method performs favorably against several state-of-the-art methods.

关键词： Semantics Feature extraction Lighting Image color analysis Benchmark testing computer vision Benchmark testing image colour analysis shadows Semantics images computer vision Lighting Feature extraction Online resources parallel network semiotics BENCHMARKS embedding

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What Is the Space of Attenuation Coefficients in Underwater computer vision? 30

What Is the Space of Attenuation Coefficients in Underwater ...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Akkaynak, Derya Treibitz, Tali Shlesinger, Tom Tamir, Raz Loya, Yossi Iluz, David Univ Haifa Haifa Israel Interuniv Inst Marine Sci Elat Israel Tel Aviv Univ Tel Aviv Israel Bar Ilan Univ Ramat Gan Israel

ISBN: (纸本)9781538604571

Underwater image reconstruction methods require the knowledge of wideband attenuation coefficients per color channel. Current estimation methods for these coefficients require specialized hardware or multiple images, and none of them leverage the multitude of existing ocean optical measurements as priors. Here, we aim to constrain the set of physically-feasible wideband attenuation coefficients in the ocean by utilizing water attenuation measured worldwide by oceanographers. We calculate the space of valid wideband effective attenuation coefficients in the 3D RGB domain and find that a bound manifold in 3-space sufficiently represents the variation from the clearest to murkiest waters. We validate our model using in situ experiments in two different optical water bodies, the Red Sea and the Mediterranean. Moreover, we show that contradictory to the common image formation model, the coefficients depend on the imaging range and object reflectance, and quantify the errors resulting from ignoring these dependencies.

关键词： Attenuation Image color analysis Oceans Sea measurements Wideband Cameras Optical attenuators

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Learning Detailed Face Reconstruction from a Single Image 30

Learning Detailed Face Reconstruction from a Single Image

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Richardson, Elad Sela, Matan Or-El, Roy Kimmel, Ron Technion Israel Inst Technol Dept Comp Sci Haifa Israel Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

ISBN: (纸本)9781538604571

Reconstructing the detailed geometric structure of a face from a given image is a key to many computer vision and graphics applications, such as motion capture and reenactment. the reconstruction task is challenging as human faces vary extensively when considering expressions, poses, textures, and intrinsic geometries. While many approaches tackle this complexity by using additional data to reconstruct the face of a single subject, extracting facial surface from a single image remains a difficult problem. As a result, single-image based methods can usually provide only a rough estimate of the facial geometry. In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image. For this purpose, we introduce an end-to-end CNN framework which derives the shape in a coarse-to-fine fashion. the proposed architecture is composed of two main blocks, a network that recovers the coarse facial geometry (CoarseNet), followed by a CNN that refines the facial features of that geometry (FineNet). the proposed networks are connected by a novel layer which renders a depth image given a mesh in 3D. Unlike object recognition and detection problems, there are no suitable datasets for training CNNs to perform face geometry reconstruction. therefore, our training regime begins with a supervised phase, based on synthetic images, followed by an unsupervised phase that uses only unconstrained facial images. the accuracy and robustness of the proposed model is demonstrated by both qualitative and quantitative evaluation tests.

关键词： Face Geometry Image reconstruction three-dimensional displays Solid modeling Training computer vision

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Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network 30

Dynamic Facial Analysis: From Bayesian Filtering to Recurren...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gu, Jinwei Yang, Xiaodong De Mello, Shalini Kautz, Jan NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781538604571

Facial analysis in videos, including head pose estimation and facial landmark localization, is key for many applications such as facial animation capture, human activity recognition, and human-computer interaction. In this paper, we propose to use a recurrent neural network (RNN) for joint estimation and tracking of facial features in videos. We are inspired by the fact that the computation performed in an RNN bears resemblance to Bayesian filters, which have been used for tracking in many previous methods for facial analysis from videos. Bayesian filters used in these methods, however, require complicated, problem-specific design and tuning. In contrast, our proposed RNN-based method avoids such tracker-engineering by learning from training data, similar to how a convolutional neural network (CNN) avoids feature-engineering for image classification. As an end-to-end network, the proposed RNN-based method provides a generic and holistic solution for joint estimation and tracking of various types of facial features from consecutive video frames. Extensive experimental results on head pose estimation and facial landmark localization from videos demonstrate that the proposed RNN-based method outperforms frame-wise models and Bayesian filtering. In addition, we create a large-scale synthetic dataset for head pose estimation, with which we achieve state-of-the-art performance on a benchmark dataset.

关键词： Bayesian filtering recurrent neural nets Videotapes joint estimation Facial Features Face Face Head tracking pose estimation computer human interaction

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Object-aware Dense Semantic Correspondence 30

Object-aware Dense Semantic Correspondence

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Fan Li, Xin Cheng, Hong Li, Jianping Chen, Leiting UESTC Sch Comp Sci & Engn Chengdu Sichuan Peoples R China UESTC Sch Automat Engn Ctr Robot Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538604571

this work aims to build pixel-to-pixel correspondences between images from the same visual class but with different geometries and visual similarities. this task is particularly challenging because (i) their visual content is similar only on the high-level structure, and (ii) background clutters keep bringing in noises. To address these problems, this paper proposes an object-aware method to estimate per-pixel correspondences from semantic to low-level by learning a classifier for each selected discriminative grid cell and guiding the localization of every pixel under the semantic constraint. Specifically, an Object-aware Hierarchical Graph (OHG) model is constructed to regulate matching consistency from one coarse grid cell containing whole object(s), to fine grid cells covering smaller semantic elements, and finally to every pixel. A guidance layer is introduced as the semantic constraint on local structure matching. In addition, we propose to learn the important high-level structure for each grid cell in an "objectness-driven" way as an alternative to hand-crafted descriptors in defining a better visual similarity. the proposed method has been extensively evaluated on various challenging benchmarks and real-world images. the results show that our method significantly outperforms the state-of-the-arts in terms of semantic flow accuracy.

关键词： Semantics Proposals Clutter Visualization computer architecture computer vision

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Switching Convolutional Neural Network for Crowd Counting 30

Switching Convolutional Neural Network for Crowd Counting

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sam, Deepak Babu Surya, Shiv Babu, R. Venkatesh Indian Inst Sci Bangalore 560012 Karnataka India

ISBN: (纸本)9781538604571

We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera view-points. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures, recurrent networks and late fusion of features from multi-column CNN with different receptive fields. We propose switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count. Patches from a grid within a crowd scene are relayed to independent CNN regressors based on crowd count prediction quality of the CNN established during training. the independent CNN regressors are designed to have different receptive fields and a switch classifier is trained to relay the crowd scene patch to the best CNN regressor. We perform extensive experiments on all major crowd counting datasets and evidence better performance compared to current state-of-the-art methods. We provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch. It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.

关键词： Switches Training computer architecture Relays Head computer vision Neural networks

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Robust Joint and Individual Variance Explained 30

Robust Joint and Individual Variance Explained

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sagonas, Christos Panagakis, Yannis Leidinger, Alina Zafeiriou, Stefanos Imperial Coll London London England Onfido London England Middlesex Univ London London England

ISBN: (纸本)9781538604571

Discovering the common (joint) and individual sub-spaces is crucial for analysis of multiple data sets, including multi-view and multi-modal data. Several statistical machine learning methods have been developed for discovering the common features across multiple data sets. the most well studied family of the methods is that of Canonical Correlation Analysis (CCA) and its variants. Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for computer vision applications. that is, it discovers only common features and not individual ones, and it is sensitive to gross errors present in visual data. Recently, efforts have been made in order to develop methods that discover individual and common components. Nevertheless, these methods are mainly applicable in two sets of data. In this paper, we investigate the use of a recently proposed statistical method, the so-called Joint and Individual Variance Explained (JIVE) method, for the recovery of joint and individual components in an arbitrary number of data sets. Since, the JIVE is not robust to gross errors, we propose alternatives, which are both robust to non-Gaussian noise of large magnitude, as well as able to automatically find the rank of the individual components. We demonstrate the effectiveness of the proposed approach to two computer vision applications, namely facial expression synthesis and face age progression in-the-wild.

关键词： Dataset crassitude error Joints Joints images computer vision non-Gaussian noise Representation of data alphameric data Exploration Variance

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Re-ranking Person Re-identification with k-reciprocal Encoding 30

Re-ranking Person Re-identification with <i>k</i>-reciprocal...

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhong, Zhun Zheng, Liang Cao, Donglin Li, Shaozi Xiamen Univ Cognit Sci Dept Xiamen Peoples R China Univ Technol Sydney Sydney NSW Australia Xiamen Univ Fujian Key Lab Brain Inspired Comp Tech & Applica Xiamen Peoples R China

ISBN: (纸本)9781538604571

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. the final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method(1).

关键词： Probes Encoding Measurement Feature extraction computer vision Robustness

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