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SRN: Side-output Residual Network for Object Symmetry Detection in the Wild 30

SRN: Side-output Residual Network for Object Symmetry Detect...

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

作者： Ke, Wei Chen, Jie Jiao, Jianbin Zhao, Guoying Ye, Qixiang Univ Chinese Acad Sci Beijing Peoples R China Univ Oulu CMVS Oulu Finland

ISBN: (纸本)9781538604571

In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. the new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. the proposed symmetry detection approach, named Side-output Residual Network (SRN), leverages output Residual Units (RUs) to fit the errors between the object symmetry ground-truth and the outputs of RUs. By stacking RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales. Experimental results validate both the benchmark and its challenging aspects related to real-world images, and the state-of-the-art performance of our symmetry detection approach. the benchmark and the code for SRN are publicly available at https://***/KevinKecc/SRN.

关键词： Benchmark testing Skeleton Image segmentation Stacking Training Color Semantics

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Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos 30

Spatio-Temporal Self-Organizing Map Deep Network for Dynamic...

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

作者： Du, Yang Yuan, Chunfeng Li, Bing Hu, Weiming Maybank, Stephen Univ Chinese Acad Sci Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Tech Natl Lab Pattern RecognitInst Automat Beijing Peoples R China Birkbeck Coll London England

ISBN: (纸本)9781538604571

In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background. this paper proposes a new Spatio-Temporal Self-Organizing Map (STSOM) deep network to detect dynamic objects in complex scenarios. the proposed approach has several contributions: First, a novel STSOM shared by all pixels in a video frame is presented to efficiently model complex background. We exploit the fact that the motions of complex background have the global variation in the space and the local variation in the time, to train STSOM using the whole frames and the sequence of a pixel over time to tackle the variance of complex background. Second, a Bayesian parameter estimation based method is presented to learn thresholds automatically for all pixels to filter out the background. Last, in order to model the complex background more accurately, we extend the single-layer STSOM to the deep network. then the background is filtered out layer by layer. Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios.

关键词： Self-organizing feature maps Object detection Videos Dynamics Bayes methods Kernel computer vision

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Instance-aware Image and Sentence Matching with Selective Multimodal LSTM 30

Instance-aware Image and Sentence Matching with Selective Mu...

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

作者： Huang, Yan Wang, Wei Wang, Liang Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Ctr Excellence Brain Sci & Intelligence Tech Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Effective image and sentence matching depends on how to well measure their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching. the sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence, by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregates them with hidden states to obtain a final matching score as the desired global similarity. Extensive experiments show that our model can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.

关键词： Dogs Roads Image color analysis Aggregates Detectors pattern recognition

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A Non-Local Low-Rank Framework for Ultrasound Speckle Reduction 30

A Non-Local Low-Rank Framework for Ultrasound Speckle Reduct...

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

作者： Zhu, Lei Fu, Chi-Wing Brown, Michael S. Heng, Pheng-Ann Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China York Univ N York ON Canada Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Virtual Real & Human Interact Te Beijing Peoples R China

ISBN: (纸本)9781538604571

'Speckle' refers to the granular patterns that occur in ultrasound images due to wave interference. Speckle removal can greatly improve the visibility of the underlying structures in an ultrasound image and enhance subsequent post-processing. We present a novel framework for speckle removal based on low-rank non-local filtering. Our approach works by first computing a guidance image that assists in the selection of candidate patches for non-local filtering in the face of significant speckles. the candidate patches are further refined using a low-rank minimization estimated using a truncated weighted nuclear norm (TWNN) and structured sparsity. We show that the proposed filtering framework produces results that outperform state-of-the-art methods both qualitatively and quantitatively. this framework also provides better segmentation results when used for pre-processing ultrasound images.

关键词： Speckle Ultrasonic imaging Noise measurement Image segmentation Minimization

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UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level vision using Diverse Datasets and Limited Memory 30

UberNet: Training a Universal Convolutional Neural Network f...

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

作者： Kokkinos, Iasonas UCL London England Facebook Artificial Intelligence Res Menlo Pk CA 94025 USA

ISBN: (纸本)9781538604571

In this work we train in an end-to-end manner a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a unified architecture. Such a network can act like a 'swiss knife' for vision tasks;we call it an "UberNet" to indicate its overarching nature. the main contribution of this work consists in handling challenges that emerge when scaling up to many tasks. We introduce techniques that facilitate (i) training a deep architecture while relying on diverse training sets and (ii) training many (potentially unlimited) tasks with a limited memory budget. this allows us to train in an end-to-end manner a unified CNN architecture that jointly handles (a) boundary detection (b) normal estimation (c) saliency estimation (d) semantic segmentation (e) human part segmentation (f) semantic boundary detection, (g) region proposal generation and object detection. We obtain competitive performance while jointly addressing all tasks in 0.7 seconds on a GPU. Our system will be made publicly available.

关键词： Training Estimation Semantics computer architecture Discrete wavelet transforms Proposals

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Image Deblurring via Extreme Channels Prior 30

Image Deblurring via Extreme Channels Prior

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

作者： Yan, Yanyang Ren, Wenqi Guo, Yuanfang Wang, Rui Cao, Xiaochun Chinese Acad Sci IIE State Key Lab Informat Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781538604571

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.

关键词： Kernel Image restoration Estimation Channel estimation Optimization Image color analysis Image edge detection

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A Regenerated Feature Extraction Method for Cross-modal Image Registration 9th

A Regenerated Feature Extraction Method for Cross-modal Imag...

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9th International conference on Brain-Inspired Cognitive Systems (BICS)

作者： Yang, Jian Wang, Qi Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Unmanned Syst Res Inst USRI Xian 710072 Shaanxi Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Shaanxi Peoples R China

ISBN: (纸本)9783030005634;9783030005627

Cross-modal image registration is an intractable problem in computer vision and pattern recognition. Inspired by that human gradually deepen to learn in the cognitive process, we present a novel method to automatically register images with different modes in this paper. Unlike most existing registrations that align images by single type of features or directly using multiple features, we employ the "regenerated" mechanism cooperated with a dynamic routing to adaptively detect features and match for different modal images. the geometry-based maximally stable extremal regions (MSER) are first implemented to fast detect non-overlapping regions as the primitive of feature regeneration, which are used to generate novel control-points using salient image disks (SIDs) operator embedded by a sub-pixel iteration. then a dynamic routing is proposed to select suitable features and match images. Experimental results on optical and multi-sensor images show that our method has a better accuracy compared to state-of-the-art approaches.

关键词： Feature regeneration MSER SIDs Image registration Dynamic routing

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Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation 30

Deep Mixture of Linear Inverse Regressions Applied to Head-P...

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

作者： Lathuiliere, Stephane Juge, Remi Mesejo, Pablo Munoz-Salinas, Rafael Horaud, Radu Inria Grenoble Rhone Alpes Montbonnot St Martin France Univ Cordoba Comp & Numer Anal Dept Cordoba Spain

ISBN: (纸本)9781538604571

Convolutional Neural Networks (ConvNets) have become the state-of-the-art for many classification and regression problems in computer vision. When it comes to regression, approaches such as measuring the Euclidean distance of target and predictions are often employed as output layer. In this paper, we propose the coupling of a Gaussian mixture of linear inverse regressions with a ConvNet, and we describe the methodological foundations and the associated algorithm to jointly train the deep network and the regression function. We test our model on the headpose estimation problem. In this particular problem, we show that inverse regression outperforms regression models currently used by state-of-the-art computer vision methods. Our method does not require the incorporation of additional data, as it is often proposed in the literature, thus it is able to work well on relatively small training datasets. Finally, it outperforms state-of-the-art methods in head-pose estimation using a widely used head-pose dataset. To the best of our knowledge, we are the first to incorporate inverse regression into deep learning for computer vision applications.

关键词： images computer vision Inverse regression Regression (Psychology) Dataset Euclidean distance

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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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Learning Non-Lambertian Object Intrinsics across ShapeNet Categories 30

Learning Non-Lambertian Object Intrinsics across ShapeNet Ca...

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

作者： Shi, Jian Dong, Yue Su, Hao Yu, Stella X. Univ Chinese Acad Sci Chinese Acad Sci Inst Software SKLCS Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Stanford Univ Stanford CA 94305 USA Univ Calif Berkeley ICSI Berkeley CA USA

ISBN: (纸本)9781538604571

We focus on the non-Lambertian object-level intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. Based on existing 3D models in the ShapeNet database, a large-scale object intrinsics database is rendered with HDR environment maps. Millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN, which can decompose an image into the product of albedo and shading components along with an additive specular component. Our CNN delivers accurate and sharp results in this classical inverse problem of computer vision. Evaluated on our realistically synthetic dataset, our method consistently outperforms the state-of-the-art by a large margin. We train and test our CNN across different object categories. Perhaps surprising especially from the CNN classification perspective, our intrinsics CNN generalizes very well across categories. Our analysis shows that feature learning at the encoder stage is more crucial for developing a universal representation across categories. We apply our model to real images and videos from Internet, and observe robust and realistic intrinsics results. Quality non-Lambertian intrinsics could open up many interesting applications such as realistic product search based on material properties and image-based albedo / specular editing.

关键词： images computer vision albedo Category artificial shade Inverse method Videotapes Imagery (Psychotherapy)

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