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  • Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-27
    Matthieu Grard, Emmanuel Dellandréa, Liming Chen

    Abstract Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining unoccluded instances by decoding a deep encoder becomes difficult, due to the translation invariance

    更新日期:2020-03-27
  • Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-26
    James Pritts, Zuzana Kukelova, Viktor Larsson, Yaroslava Lochman, Ondřej Chum

    Abstract This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from the image of rigidly-transformed coplanar features. The solvers work on scenes without straight lines and, in general, relax strong assumptions about scene content made by the state of the art. The proposed solvers use the affine invariant that coplanar repeats have the same

    更新日期:2020-03-27
  • Enhanced Balanced Min Cut
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-26
    Xiaojun Chen, Weijun Hong, Feiping Nie, Joshua Zhexue Huang, Li Shen

    Abstract Spectral clustering is a hot topic and many spectral clustering algorithms have been proposed. These algorithms usually solve the discrete cluster indicator matrix by relaxing the original problems, obtaining the continuous solution and finally obtaining a discrete solution that is close to the continuous solution. However, such methods often result in a non-optimal solution to the original

    更新日期:2020-03-27
  • A Survey of Deep Facial Attribute Analysis
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-24
    Xin Zheng, Yanqing Guo, Huaibo Huang, Yi Li, Ran He

    Abstract Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which

    更新日期:2020-03-24
  • Adversarial Confidence Learning for Medical Image Segmentation and Synthesis
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-21
    Dong Nie, Dinggang Shen

    Abstract Generative adversarial networks (GAN) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis. In these works, adversarial learning is directly applied to the original supervised segmentation (synthesis) networks. The usage of adversarial learning is effective in improving visual perception performance since adversarial learning works as realistic

    更新日期:2020-03-22
  • Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-20
    Sai Rajeswar, Fahim Mannan, Florian Golemo, Jérôme Parent-Lévesque, David Vazquez, Derek Nowrouzezahrai, Aaron Courville

    Abstract We infer and generate three-dimensional (3D) scene information from a single input image and without supervision. This problem is under-explored, with most prior work relying on supervision from, e.g., 3D ground-truth, multiple images of a scene, image silhouettes or key-points. We propose Pix2Shape, an approach to solve this problem with four component: (i) an encoder that infers the latent

    更新日期:2020-03-21
  • Learning Multifunctional Binary Codes for Personalized Image Retrieval
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-17
    Haomiao Liu, Ruiping Wang, Shiguang Shan, Xilin Chen

    Abstract Due to the highly complex semantic information of images, even with the same query image, the expected content-based image retrieval results could be very different and personalized in different scenarios. However, most existing hashing methods only preserve one single type of semantic similarity, making them incapable of addressing such realistic retrieval tasks. To deal with this problem

    更新日期:2020-03-19
  • Is There Anything New to Say About SIFT Matching?
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-17
    Fabio Bellavia, Carlo Colombo

    Abstract SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering

    更新日期:2020-03-19
  • CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-17
    Yunan Li, Jun Wan, Qiguang Miao, Sergio Escalera, Huijuan Fang, Huizhou Chen, Xiangda Qi, Guodong Guo

    Abstract First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including

    更新日期:2020-03-19
  • Semi-online Multi-people Tracking by Re-identification
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-17
    Long Lan, Xinchao Wang, Gang Hua, Thomas S. Huang, Dacheng Tao

    Abstract In this paper, we propose a novel semi-online approach to tracking multiple people. In contrast to conventional offline approaches that take the whole image sequence as input, our semi-online approach tracks people in a frame-by-frame manner by exploring the time, space and multi-camera relationship of detection hypotheses in the near future frames. We cast the multi-people tracking task as

    更新日期:2020-03-19
  • The Open Images Dataset V4
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-13
    Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, Vittorio Ferrari

    Abstract We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding

    更新日期:2020-03-16
  • Light Structure from Pin Motion: Geometric Point Light Source Calibration
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-13
    Hiroaki Santo, Michael Waechter, Wen-Yan Lin, Yusuke Sugano, Yasuyuki Matsushita

    Abstract We present a method for geometric point light source calibration. Unlike prior works that use Lambertian spheres, mirror spheres, or mirror planes, we use a calibration target consisting of a plane and small shadow casters at unknown positions above the plane. We show that shadow observations from a moving calibration target under a fixed light follow the principles of pinhole camera geometry

    更新日期:2020-03-16
  • MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-05
    Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Peer-Timo Bremer

    Abstract In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As a result, they have been widely adopted across a variety of applications, ranging from challenging inverse problems like image completion, to problems such as anomaly detection and adversarial defense. A recurring

    更新日期:2020-03-06
  • Deep Image Prior
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-04
    Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

    Abstract Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any

    更新日期:2020-03-04
  • MAP Inference Via $$\ell _2$$ℓ2 -Sphere Linear Program Reformulation
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-04
    Baoyuan Wu, Li Shen, Tong Zhang, Bernard Ghanem

    Abstract Maximum a posteriori (MAP) inference is an important task for graphical models. Due to complex dependencies among variables in realistic models, finding an exact solution for MAP inference is often intractable. Thus, many approximation methods have been developed, among which the linear programming (LP) relaxation based methods show promising performance. However, one major drawback of LP

    更新日期:2020-03-04
  • Discriminator Feature-Based Inference by Recycling the Discriminator of GANs
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-04
    Duhyeon Bang, Seoungyoon Kang, Hyunjung Shim

    Abstract Generative adversarial networks (GANs) successfully generate high quality data by learning a mapping from a latent vector to the data. Various studies assert that the latent space of a GAN is semantically meaningful and can be utilized for advanced data analysis and manipulation. To analyze the real data in the latent space of a GAN, it is necessary to build an inference mapping from the data

    更新日期:2020-03-04
  • Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in CNNs
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-03-04
    Timo Hackel, Mikhail Usvyatsov, Silvano Galliani, Jan D. Wegner, Konrad Schindler

    Abstract Convolutional neural networks (CNNs) are a powerful tool for pattern recognition and computer vision, but they do not scale well to higher-dimensional inputs, because of the associated memory demands for storing and manipulating high-dimensional tensors. This work starts from the observation that higher-dimensional data, like for example 3D voxel volumes, are sparsely populated. CNNs naturally

    更新日期:2020-03-04
  • Convolutional Networks with Adaptive Inference Graphs
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-06-21
    Andreas Veit, Serge Belongie

    Abstract Do convolutional networks really need a fixed feed-forward structure? What if, after identifying the high-level concept of an image, a network could move directly to a layer that can distinguish fine-grained differences? Currently, a network would first need to execute sometimes hundreds of intermediate layers that specialize in unrelated aspects. Ideally, the more a network already knows

    更新日期:2020-03-04
  • Group Normalization
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-07-22
    Yuxin Wu, Kaiming He

    Abstract Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems—BN’s error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN’s usage for training larger models and transferring features to computer

    更新日期:2020-03-04
  • Layout2image: Image Generation from Layout
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-24
    Bo Zhao, Weidong Yin, Lili Meng, Leonid Sigal

    Abstract Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges, we propose a novel approach for

    更新日期:2020-02-24
  • Deep Neural Network Augmentation: Generating Faces for Affect Analysis
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-22
    Dimitrios Kollias, Shiyang Cheng, Evangelos Ververas, Irene Kotsia, Stefanos Zafeiriou

    Abstract This paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (i.e., anger, disgust, fear, joy, sadness and surprise), or in terms of valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the emotion activation). The proposed approach accepts the following inputs:(i) a neutral 2D image of a person; (ii) a basic

    更新日期:2020-02-23
  • Tensorized Multi-view Subspace Representation Learning
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-20
    Changqing Zhang, Huazhu Fu, Jing Wang, Wen Li, Xiaochun Cao, Qinghua Hu

    Abstract Self-representation based subspace learning has shown its effectiveness in many applications. In this paper, we promote the traditional subspace representation learning by simultaneously taking advantages of multiple views and prior constraint. Accordingly, we establish a novel algorithm termed as Tensorized Multi-view Subspace Representation Learning. To exploit different views, the subspace

    更新日期:2020-02-20
  • Anchor-Based Self-Ensembling for Semi-Supervised Deep Pairwise Hashing
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-18
    Xiaoshuang Shi, Zhenhua Guo, Fuyong Xing, Yun Liang, Lin Yang

    Abstract Deep hashing has attracted considerable attention to tackle large-scale retrieval tasks, because of automatic and powerful feature extraction of convolutional neural networks and the gain of hashing in computation and storage costs. Most current supervised deep hashing methods only utilize the semantic information of labeled data without exploiting unlabeled data. However, data annotation

    更新日期:2020-02-19
  • Multi-task Generative Adversarial Network for Detecting Small Objects in the Wild
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-18
    Yongqiang Zhang, Yancheng Bai, Mingli Ding, Bernard Ghanem

    Abstract Object detection results have been rapidly improved over a short period of time with the development of deep convolutional neural networks. Although impressive results have been achieved on large/medium sized objects, the performance on small objects is far from satisfactory and one of remaining open challenges is detecting small object in unconstrained conditions (e.g. COCO and WIDER FACE

    更新日期:2020-02-18
  • Unified Binary Generative Adversarial Network for Image Retrieval and Compression
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-18
    Jingkuan Song, Tao He, Lianli Gao, Xing Xu, Alan Hanjalic, Heng Tao Shen

    Abstract Binary codes have often been deployed to facilitate large-scale retrieval tasks, but not that often for image compression. In this paper, we propose a unified framework, BGAN+, that restricts the input noise variable of generative adversarial networks to be binary and conditioned on the features of each input image, and simultaneously learns two binary representations per image: one for image

    更新日期:2020-02-18
  • Bottom-Up Scene Text Detection with Markov Clustering Networks
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-10
    Zichuan Liu, Guosheng Lin, Wang Ling Goh

    Abstract A novel detection framework named Markov Clustering Network (MCN) is proposed for fast and robust scene text detection. Different from the traditional top-down scene text detection approaches that inherit from the classic object detection, MCN detects scene text objects in a bottom-up manner. MCN predicts instance-level bounding boxes by firstly converting an image into a stochastic flow graph

    更新日期:2020-02-10
  • DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-03
    Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao, Jia-Bin Huang, Yu-Ding Lu, Maneesh Singh, Ming-Hsuan Yang

    Abstract Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: (1) lack of aligned training pairs and (2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images. To synthesize diverse outputs, we

    更新日期:2020-02-04
  • Visual Social Relationship Recognition
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-03
    Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli

    Abstract Social relationships form the basis of social structure of humans. Developing computational models to understand social relationships from visual data is essential for building intelligent machines that can better interact with humans in a social environment. In this work, we study the problem of visual social relationship recognition in images. We propose a dual-glance model for social relationship

    更新日期:2020-02-04
  • VOSTR: Video Object Segmentation via Transferable Representations
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-03
    Yi-Wen Chen, Yi-Hsuan Tsai, Yen-Yu Lin, Ming-Hsuan Yang

    Abstract In order to learn video object segmentation models, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations

    更新日期:2020-02-04
  • RGB-IR Person Re-identification by Cross-Modality Similarity Preservation
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-02-03
    Ancong Wu, Wei-Shi Zheng, Shaogang Gong, Jianhuang Lai

    Abstract Person re-identification (Re-ID) is an important problem in video surveillance for matching pedestrian images across non-overlapping camera views. Currently, most works focus on RGB-based Re-ID. However, RGB images are not well suited to a dark environment; consequently, infrared (IR) imaging becomes necessary for indoor scenes with low lighting and 24-h outdoor scene surveillance systems

    更新日期:2020-02-04
  • Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-30
    Xiang Wang, Sifei Liu, Huimin Ma, Ming-Hsuan Yang

    Abstract Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. Recent methods have exploited classification networks to localize objects by selecting regions with strong response. While such response map provides sparse information, however, there exist strong pairwise relations between pixels in natural images, which can be utilized

    更新日期:2020-01-31
  • EdgeStereo: An Effective Multi-task Learning Network for Stereo Matching and Edge Detection
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-28
    Xiao Song, Xu Zhao, Liangji Fang, Hanwen Hu, Yizhou Yu

    Abstract Recently, leveraging on the development of end-to-end convolutional neural networks, deep stereo matching networks have achieved remarkable performance far exceeding traditional approaches. However, state-of-the-art stereo frameworks still have difficulties at finding correct correspondences in texture-less regions, detailed structures, small objects and near boundaries, which could be alleviated

    更新日期:2020-01-30
  • A Simple and Light-Weight Attention Module for Convolutional Neural Networks
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-28
    Jongchan Park, Sanghyun Woo, Joon-Young Lee, In So Kweon

    Abstract Many aspects of deep neural networks, such as depth, width, or cardinality, have been studied to strengthen the representational power. In this work, we study the effect of attention in convolutional neural networks and present our idea in a simple self-contained module, called Bottleneck Attention Module (BAM). Given an intermediate feature map, BAM efficiently produces the attention map

    更新日期:2020-01-30
  • Discriminative Training of Conditional Random Fields with Probably Submodular Constraints
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-22
    Maxim Berman, Matthew B. Blaschko

    Abstract Problems of segmentation, denoising, registration and 3D reconstruction are often addressed with the graph cut algorithm. However, solving an unconstrained graph cut problem is NP-hard. For tractable optimization, pairwise potentials have to fulfill the submodularity inequality. In our learning paradigm, pairwise potentials are created as the dot product of a learned vector w with positive

    更新日期:2020-01-23
  • Handwritten Mathematical Expression Recognition via Paired Adversarial Learning
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-21
    Jin-Wen Wu, Fei Yin, Yan-Ming Zhang, Xu-Yao Zhang, Cheng-Lin Liu

    Abstract Recognition of handwritten mathematical expressions (MEs) is an important problem that has wide applications in practice. Handwritten ME recognition is challenging due to the variety of writing styles and ME formats. As a result, recognizers trained by optimizing the traditional supervision loss do not perform satisfactorily. To improve the robustness of the recognizer with respect to writing

    更新日期:2020-01-22
  • Simultaneous Deep Stereo Matching and Dehazing with Feature Attention
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-21
    Taeyong Song, Youngjung Kim, Changjae Oh, Hyunsung Jang, Namkoo Ha, Kwanghoon Sohn

    Abstract Unveiling the dense correspondence under the haze layer remains a challenging task, since the scattering effects result in less distinctive image features. Contrarily, dehazing is often confused by the airlight-albedo ambiguity which cannot be resolved independently at each pixel. In this paper, we introduce a deep convolutional neural network that simultaneously estimates a disparity and

    更新日期:2020-01-22
  • Learning an Evolutionary Embedding via Massive Knowledge Distillation
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-21
    Xiang Wu, Ran He, Yibo Hu, Zhenan Sun

    Abstract Knowledge distillation methods aim at transferring knowledge from a large powerful teacher network to a small compact student one. These methods often focus on close-set classification problems and matching features between teacher and student networks from a single sample. However, many real-world classification problems are open-set. This paper proposes an Evolutionary Embedding Learning

    更新日期:2020-01-22
  • Gated Fusion Network for Degraded Image Super Resolution
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-13
    Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang, Ming-Hsuan Yang

    Single image super resolution aims to enhance image quality with respect to spatial content, which is a fundamental task in computer vision. In this work, we address the task of single frame super resolution with the presence of image degradation, e.g., blur, haze, or rain streaks. Due to the limitations of frame capturing and formation processes, image degradation is inevitable, and the artifacts

    更新日期:2020-01-13
  • Siamese Dense Network for Reflection Removal with Flash and No-Flash Image Pairs
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-09
    Yakun Chang, Cheolkon Jung, Jun Sun, Fengqiao Wang

    Abstract This work addresses the reflection removal with flash and no-flash image pairs to separate reflection from transmission. When objects are covered by glass, the no-flash image usually contains reflection, and thus flash is used to enhance transmission details. However, the flash image suffers from the specular highlight on the glass surface caused by flash. In this paper, we propose a siamese

    更新日期:2020-01-09
  • Fine-Grained Person Re-identification
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-08
    Jiahang Yin, Ancong Wu, Wei-Shi Zheng

    Abstract Person re-identification (re-id) plays a critical role in tracking people via surveillance systems by matching people across non-overlapping camera views at different locations. Although most re-id methods largely depend on the appearance features of a person, such methods always assume that the appearance information (particularly color) is distinguishable. However, distinguishing people

    更新日期:2020-01-08
  • Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-02
    Domen Tabernik, Matej Kristan, Aleš Leonardis

    Abstract Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, which has to be manually set to accommodate a specific task. Standard solutions involve large kernels, down/up-sampling and dilated convolutions. These require testing a variety of dilation and down/up-sampling factors and result

    更新日期:2020-01-02
  • Learning Multi-human Optical Flow
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-02
    Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas, Siyu Tang, Javier Romero, Michael J. Black

    The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However, the training data used by them does not cover the domain of human motion. Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset. We use a 3D model of the human body

    更新日期:2020-01-02
  • Text2Sign: Towards Sign Language Production Using Neural Machine Translation and Generative Adversarial Networks
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2020-01-02
    Stephanie Stoll, Necati Cihan Camgoz, Simon Hadfield, Richard Bowden

    We present a novel approach to automatic Sign Language Production using recent developments in Neural Machine Translation (NMT), Generative Adversarial Networks, and motion generation. Our system is capable of producing sign videos from spoken language sentences. Contrary to current approaches that are dependent on heavily annotated data, our approach requires minimal gloss and skeletal level annotations

    更新日期:2020-01-02
  • Scalable Person Re-Identification by Harmonious Attention
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-23
    Wei Li, Xiatian Zhu, Shaogang Gong

    Abstract Existing person re-identification (re-id) deep learning methods rely heavily on the utilisation of large and computationally expensive convolutional neural networks. They are therefore not scalable to large scale re-id deployment scenarios with the need of processing a large amount of surveillance video data, due to the lengthy inference process with high computing costs. In this work, we

    更新日期:2019-12-23
  • Representation Learning on Unit Ball with 3D Roto-translational Equivariance
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-21
    Sameera Ramasinghe, Salman Khan, Nick Barnes, Stephen Gould

    Convolution is an integral operation that defines how the shape of one function is modified by another function. This powerful concept forms the basis of hierarchical feature learning in deep neural networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topological spaces—such as a sphere (\(\mathbb {S}^2\)) or a unit ball (\(\mathbb {B}^3\))—entails

    更新日期:2019-12-21
  • Rectified Wing Loss for Efficient and Robust Facial Landmark Localisation with Convolutional Neural Networks
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-17
    Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Xiao-Jun Wu

    Efficient and robust facial landmark localisation is crucial for the deployment of real-time face analysis systems. This paper presents a new loss function, namely Rectified Wing (RWing) loss, for regression-based facial landmark localisation with Convolutional Neural Networks (CNNs). We first systemically analyse different loss functions, including L2, L1 and smooth L1. The analysis suggests that

    更新日期:2019-12-18
  • A Face Fairness Framework for 3D Meshes
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-17
    Sk. Mohammadul Haque, Venu Madhav Govindu

    In this paper, we present a face fairness framework for 3D meshes that preserves the regular shape of faces and is applicable to a variety of 3D mesh restoration tasks. Specifically, we present a number of desirable properties for any mesh restoration method and show that our framework satisfies them. We then apply our framework to two different tasks—mesh-denoising and mesh-refinement, and present

    更新日期:2019-12-18
  • Real-Time Multi-person Motion Capture from Multi-view Video and IMUs
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-17
    Charles Malleson, John Collomosse, Adrian Hilton

    A real-time motion capture system is presented which uses input from multiple standard video cameras and inertial measurement units (IMUs). The system is able to track multiple people simultaneously and requires no optical markers, specialized infra-red cameras or foreground/background segmentation, making it applicable to general indoor and outdoor scenarios with dynamic backgrounds and lighting.

    更新日期:2019-12-18
  • Discriminative Region Proposal Adversarial Network for High-Quality Image-to-Image Translation
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-17
    Chao Wang, Wenjie Niu, Yufeng Jiang, Haiyong Zheng, Zhibin Yu, Zhaorui Gu, Bing Zheng

    Image-to-image translation has been made much progress with embracing Generative Adversarial Networks (GANs). However, it’s still very challenging for translation tasks that require high quality, especially at high-resolution and photo-reality. In this work, we present Discriminative Region Proposal Adversarial Network (DRPAN) for high-quality image-to-image translation. We decompose the image-to-image

    更新日期:2019-12-18
  • GMS: Grid-Based Motion Statistics for Fast, Ultra-robust Feature Correspondence
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-17
    Jia-Wang Bian, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, Ian Reid

    Feature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational

    更新日期:2019-12-18
  • Disentangled Representation Learning of Makeup Portraits in the Wild
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-11
    Yi Li, Huaibo Huang, Jie Cao, Ran He, Tieniu Tan

    Makeup studies have recently caught much attention in computer version. Two of the typical tasks are makeup-invariant face verification and makeup transfer. Although having experienced remarkable progress, both tasks remain challenging, especially encountering data in the wild. In this paper, we propose a disentangled feature learning strategy to fulfil both tasks in a single generative network. Overall

    更新日期:2019-12-11
  • Correction to: Model-Based Robot Imitation with Future Image Similarity
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-09
    A. Wu, A. J. Piergiovanni, M. S. Ryoo

    The acknowledgement section was omitted in the original version of this article, which is given below.

    更新日期:2019-12-09
  • SSN: Learning Sparse Switchable Normalization via SparsestMax
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-09
    Wenqi Shao, Jingyu Li, Jiamin Ren, Ruimao Zhang, Xiaogang Wang, Ping Luo

    Normalization method deals with parameters training of convolution neural networks (CNNs) in which there are often multiple convolution layers. Despite the fact that layers in CNN are not homogeneous in the role they play at representing a prediction function, existing works often employ identical normalizer in different layers, making performance away from idealism. To tackle this problem and further

    更新日期:2019-12-09
  • Pixelated Semantic Colorization
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-07
    Jiaojiao Zhao, Jungong Han, Ling Shao, Cees G. M. Snoek

    While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories

    更新日期:2019-12-07
  • The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-12-03
    Hongyang Yu, Guorong Li, Weigang Zhang, Qingming Huang, Dawei Du, Qi Tian, Nicu Sebe

    With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. To facilitate research in the UAV field, this paper presents a UAV dataset with 100 videos featuring approximately 2700 vehicles recorded under unconstrained conditions and 840k manually

    更新日期:2019-12-04
  • Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-11-27
    Guo-Jun Qi

    In this paper, we present the Lipschitz regularization theory and algorithms for a novel Loss-Sensitive Generative Adversarial Network (LS-GAN). Specifically, it trains a loss function to distinguish between real and fake samples by designated margins, while learning a generator alternately to produce realistic samples by minimizing their losses. The LS-GAN further regularizes its loss function with

    更新日期:2019-11-28
  • Refractive Two-View Reconstruction for Underwater 3D Vision
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-11-18
    François Chadebecq, Francisco Vasconcelos, René Lacher, Efthymios Maneas, Adrien Desjardins, Sébastien Ourselin, Tom Vercauteren, Danail Stoyanov

    Recovering 3D geometry from cameras in underwater applications involves the Refractive Structure-from-Motion problem where the non-linear distortion of light induced by a change of medium density invalidates the single viewpoint assumption. The pinhole-plus-distortion camera projection model suffers from a systematic geometric bias since refractive distortion depends on object distance. This leads

    更新日期:2019-11-18
  • HetConv: Beyond Homogeneous Convolution Kernels for Deep CNNs
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-11-18
    Pravendra Singh, Vinay Kumar Verma, Piyush Rai, Vinay P. Namboodiri

    While usage of convolutional neural networks (CNN) is widely prevalent, methods proposed so far always have considered homogeneous kernels for this task. In this paper, we propose a new type of convolution operation using heterogeneous kernels. The proposed Heterogeneous Kernel-Based Convolution (HetConv) reduces the computation (FLOPs) and the number of parameters as compared to standard convolution

    更新日期:2019-11-18
  • 3D Fluid Flow Estimation with Integrated Particle Reconstruction
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-11-13
    Katrin Lasinger, Christoph Vogel, Thomas Pock, Konrad Schindler

    The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the acquired multi-view video in two separate steps, utilizing either a pure Eulerian or pure Lagrangian approach. Eulerian methods perform a voxel-based reconstruction of particles per time

    更新日期:2019-11-13
  • Learning to Draw Sight Lines
    Int. J. Comput. Vis. (IF 6.071) Pub Date : 2019-11-12
    Hao Zhao, Ming Lu, Anbang Yao, Yurong Chen, Li Zhang

    In this paper, we are concerned with the task of gaze following. Given a scene (e.g. a girl playing soccer on the field) and a human subject’s head position, this task aims to infer where she is looking (e.g. at the soccer ball). An existing method adopts a saliency model conditioned on the head position. However, this methodology is intrinsically troubled with dataset bias issues, which we will reveal

    更新日期:2019-11-13
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