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  • Towards Photo-Realistic Facial Expression Manipulation
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-28
    Zhenglin Geng, Chen Cao, Sergey Tulyakov

    We present a method for photo-realistic face manipulation. Given a single RGB face image with an arbitrary expression, our method can synthesize another arbitrary expression of the same person. To achieve this, we first fit a 3D face model and disentangle the face into its texture and shape. We then train separate networks in each of these spaces. In texture space, we use a conditional generative network

    更新日期:2020-09-23
  • Unsupervised Deep Representation Learning for Real-Time Tracking
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-09-21
    Ning Wang, Wengang Zhou, Yibing Song, Chao Ma, Wei Liu, Houqiang Li

    The advancement of visual tracking has continuously been brought by deep learning models. Typically, supervised learning is employed to train these models with expensive labeled data. In order to reduce the workload of manual annotation and learn to track arbitrary objects, we propose an unsupervised learning method for visual tracking. The motivation of our unsupervised learning is that a robust tracker

    更新日期:2020-09-22
  • Face Image Reflection Removal
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-09-15
    Renjie Wan, Boxin Shi, Haoliang Li, Ling-Yu Duan, Alex C. Kot

    Face images captured through glass are usually contaminated by reflections. The low-transmitted reflections make the reflection removal more challenging than for general scenes because important facial features would be completely occluded. In this paper, we propose and solve the face image reflection removal problem. We recover the important facial structures by incorporating inpainting ideas into

    更新日期:2020-09-15
  • Rain Rendering for Evaluating and Improving Robustness to Bad Weather
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-09-14
    Maxime Tremblay, Shirsendu Sukanta Halder, Raoul de Charette, Jean-François Lalonde

    Rain fills the atmosphere with water particles, which breaks the common assumption that light travels unaltered from the scene to the camera. While it is well-known that rain affects computer vision algorithms, quantifying its impact is difficult. In this context, we present a rain rendering pipeline that enables the systematic evaluation of common computer vision algorithms to controlled amounts of

    更新日期:2020-09-15
  • A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-09-14
    Lyndon Chan, Mahdi S. Hosseini, Konstantinos N. Plataniotis

    Recently proposed methods for weakly-supervised semantic segmentation have achieved impressive performance in predicting pixel classes despite being trained with only image labels which lack positional information. Because image annotations are cheaper and quicker to generate, weak supervision is more practical than full supervision for training segmentation algorithms. These methods have been predominantly

    更新日期:2020-09-14
  • Volume Sweeping: Learning Photoconsistency for Multi-View Shape Reconstruction
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-09-10
    Vincent Leroy, Jean-Sébastien Franco, Edmond Boyer

    We propose a full study and methodology for multi-view stereo reconstruction with performance capture data. Multi-view 3D reconstruction has largely been studied with general, high resolution and high texture content inputs, where classic low-level feature extraction and matching are generally successful. However in performance capture scenarios, texture content is limited by wider angle shots resulting

    更新日期:2020-09-10
  • J $$\hat{\text {A}}$$ A ^ A-Net: Joint Facial Action Unit Detection and Face Alignment Via Adaptive Attention
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-09-10
    Zhiwen Shao, Zhilei Liu, Jianfei Cai, Lizhuang Ma

    Facial action unit (AU) detection and face alignment are two highly correlated tasks, since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. However, most existing AU detection works handle the two tasks independently by treating face alignment as a preprocessing, and often use landmarks to predefine a fixed region or attention

    更新日期:2020-09-10
  • Beyond Covariance: SICE and Kernel Based Visual Feature Representation
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-09-10
    Jianjia Zhang, Lei Wang, Luping Zhou, Wanqing Li

    The past several years have witnessed increasing research interest on covariance-based feature representation. Originally proposed as a region descriptor, it has now been used as a general representation in various recognition tasks, demonstrating promising performance. However, covariance matrix has some inherent shortcomings such as singularity in the case of small sample, limited capability in modeling

    更新日期:2020-09-10
  • Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-31
    Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool

    The role of robots in society keeps expanding, bringing with it the necessity of interacting and communicating with humans. In order to keep such interaction intuitive, we provide automatic wayfinding based on verbal navigational instructions. Our first contribution is the creation of a large-scale dataset with verbal navigation instructions. To this end, we have developed an interactive visual navigation

    更新日期:2020-08-31
  • Pixel-Wise Crowd Understanding via Synthetic Data
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-30
    Qi Wang, Junyu Gao, Wei Lin, Yuan Yuan

    Crowd analysis via computer vision techniques is an important topic in the field of video surveillance, which has wide-spread applications including crowd monitoring, public safety, space design and so on. Pixel-wise crowd understanding is the most fundamental task in crowd analysis because of its finer results for video sequences or still images than other analysis tasks. Unfortunately, pixel-level

    更新日期:2020-08-30
  • DeepVS2.0: A Saliency-Structured Deep Learning Method for Predicting Dynamic Visual Attention
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-28
    Lai Jiang, Mai Xu, Zulin Wang, Leonid Sigal

    Deep neural networks (DNNs) have exhibited great success in image saliency prediction. However, few works apply DNNs to predict the saliency of generic videos. In this paper, we propose a novel DNN-based video saliency prediction method, called DeepVS2.0. Specifically, we establish a large-scale eye-tracking database of videos (LEDOV), which provides sufficient data to train the DNN models for predicting

    更新日期:2020-08-28
  • Scene Text Detection and Recognition: The Deep Learning Era
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-27
    Shangbang Long, Xin He, Cong Yao

    With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inevitably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, methodology

    更新日期:2020-08-27
  • GADE: A Generative Adversarial Approach to Density Estimation and its Applications
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-19
    M. Ehsan Abbasnejad, Javen Shi, Anton van den Hengel, Lingqiao Liu

    Density estimation is a challenging unsupervised learning problem. Current maximum likelihood approaches for density estimation are either restrictive or incapable of producing high-quality samples. On the other hand, likelihood-free models such as generative adversarial networks, produce sharp samples without a density model. The lack of a density estimate limits the applications to which the sampled

    更新日期:2020-08-19
  • Temporally Coherent General Dynamic Scene Reconstruction
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-18
    Armin Mustafa, Marco Volino, Hansung Kim, Jean-Yves Guillemaut, Adrian Hilton

    Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints. This paper introduces a general approach to obtain a 4D representation of complex dynamic scenes from multi-view wide-baseline static or moving cameras without prior knowledge of the scene structure

    更新日期:2020-08-18
  • Solving Rolling Shutter 3D Vision Problems using Analogies with Non-rigidity
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-17
    Yizhen Lao, Omar Ait-Aider, Adrien Bartoli

    We propose an original approach to absolute pose and structure-from-motion (SfM) which handles rolling shutter (RS) effects. Unlike most existing methods which either augment global shutter projection with velocity parameters or impose continuous time and motion through pose interpolation, we use local differential constraints. These are established by drawing analogies with non-rigid 3D vision techniques

    更新日期:2020-08-17
  • A Camera Model for Line-Scan Cameras with Telecentric Lenses
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-12
    Carsten Steger, Markus Ulrich

    We propose a camera model for line-scan cameras with telecentric lenses. The camera model assumes a linear relative motion with constant velocity between the camera and the object. It allows to model lens distortions, while supporting arbitrary positions of the line sensor with respect to the optical axis. We comprehensively examine the degeneracies of the camera model and propose methods to handle

    更新日期:2020-08-12
  • Unsupervised Domain Adaptation in the Wild via Disentangling Representation Learning
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-11
    Haoliang Li, Renjie Wan, Shiqi Wang, Alex C. Kot

    Most recently proposed unsupervised domain adaptation algorithms attempt to learn domain invariant features by confusing a domain classifier through adversarial training. In this paper, we argue that this may not be an optimal solution in the real-world setting (a.k.a. in the wild) as the difference in terms of label information between domains has been largely ignored. As labeled instances are not

    更新日期:2020-08-11
  • Densifying Supervision for Fine-Grained Visual Comparisons
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-09
    Aron Yu, Kristen Grauman

    Detecting subtle differences in visual attributes requires inferring which of two images exhibits a property more, e.g., which face is smiling slightly more, or which shoe is slightly more sporty. While valuable for applications ranging from biometrics to online shopping, fine-grained attributes are challenging to learn. Unlike traditional recognition tasks, the supervision is inherently comparative

    更新日期:2020-08-09
  • Image Matching from Handcrafted to Deep Features: A Survey
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-04
    Jiayi Ma, Xingyu Jiang, Aoxiang Fan, Junjun Jiang, Junchi Yan

    As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions

    更新日期:2020-08-04
  • View Transfer on Human Skeleton Pose: Automatically Disentangle the View-Variant and View-Invariant Information for Pose Representation Learning
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-08-01
    Qiang Nie, Yunhui Liu

    Learning a good pose representation is significant for many applications, such as human pose estimation and action recognition. However, the representations learned by most approaches are not intrinsic and their transferability in different datasets and different tasks is limited. In this paper, we introduce a method to learn a versatile representation, which is capable of recovering unseen corrupted

    更新日期:2020-08-01
  • Multi-task Compositional Network for Visual Relationship Detection
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-30
    Yibing Zhan, Jun Yu, Ting Yu, Dacheng Tao

    Previous methods treat visual relationship detection as a combination of object detection and predicate detection. However, natural images likely contain hundreds of objects and thousands of object pairs. Relying only on object detection and predicate detection is insufficient for effective visual relationship detection because the significant relationships are easily overwhelmed by the dominant less-significant

    更新日期:2020-07-30
  • Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-Based Image Retrieval
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-29
    Anjan Dutta, Zeynep Akata

    Low-shot sketch-based image retrieval is an emerging task in computer vision, allowing to retrieve natural images relevant to hand-drawn sketch queries that are rarely seen during the training phase. Related prior works either require aligned sketch-image pairs that are costly to obtain or inefficient memory fusion layer for mapping the visual information to a semantic space. In this paper, we address

    更新日期:2020-07-29
  • Zero-Shot Object Detection: Joint Recognition and Localization of Novel Concepts
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-24
    Shafin Rahman, Salman H. Khan, Fatih Porikli

    Zero shot learning (ZSL) identifies unseen objects for which no training images are available. Conventional ZSL approaches are restricted to a recognition setting where each test image is categorized into one of several unseen object classes. We posit that this setting is ill-suited for real-world applications where unseen objects appear only as a part of a complete scene, warranting both ‘recognition’

    更新日期:2020-07-24
  • Video Based Face Recognition by Using Discriminatively Learned Convex Models
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-24
    Hakan Cevikalp, Golara Ghorban Dordinejad

    A majority of the image set based face recognition methods use a generatively learned model for each person that is learned independently by ignoring the other persons in the gallery set. In contrast to these methods, this paper introduces a novel method that searches for discriminative convex models that best fit to an individual’s face images but at the same time are as far as possible from the images

    更新日期:2020-07-24
  • Rooted Spanning Superpixels
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-20
    Dengfeng Chai

    This paper proposes a new approach for superpixel segmentation. It is formulated as finding a rooted spanning forest of a graph with respect to some roots and a path-cost function. The underlying graph represents an image, the roots serve as seeds for segmentation, each pixel is connected to one seed via a path, the path-cost function measures both the color similarity and spatial closeness between

    更新日期:2020-07-20
  • Long-Short Temporal–Spatial Clues Excited Network for Robust Person Re-identification
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-15
    Shuai Li, Wenfeng Song, Zheng Fang, Jiaying Shi, Aimin Hao, Qinping Zhao, Hong Qin

    Directly benefiting from the rapid advancement of deep learning methods, person re-identification (Re-ID) applications have been widespread with remarkable successes in recent years. Nevertheless, cross-scene Re-ID is still hindered by large view variation, since it is challenging to effectively exploit and leverage the temporal clues due to heavy computational burden and the difficulty in flexibly

    更新日期:2020-07-15
  • Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-15
    Haozhe Xie, Hongxun Yao, Shengping Zhang, Shangchen Zhou, Wenxiu Sun

    Recovering the 3D shape of an object from single or multiple images with deep neural networks has been attracting increasing attention in the past few years. Mainstream works (e.g. 3D-R2N2) use recurrent neural networks (RNNs) to sequentially fuse feature maps of input images. However, RNN-based approaches are unable to produce consistent reconstruction results when given the same input images with

    更新日期:2020-07-15
  • RoCGAN: Robust Conditional GAN
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-14
    Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou

    Conditional image generation lies at the heart of computer vision and conditional generative adversarial networks (cGAN) have recently become the method of choice for this task, owing to their superior performance. The focus so far has largely been on performance improvement, with little effort in making cGANs more robust to noise. However, the regression (of the generator) might lead to arbitrarily

    更新日期:2020-07-14
  • Learning the spatiotemporal variability in longitudinal shape data sets
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-02
    Alexandre Bône, Olivier Colliot, Stanley Durrleman

    In this paper, we propose a generative statistical model to learn the spatiotemporal variability in longitudinal shape data sets, which contain repeated observations of a set of objects or individuals over time. From all the short-term sequences of individual data, the method estimates a long-term normative scenario of shape changes and a tubular coordinate system around this trajectory. Each individual

    更新日期:2020-07-02
  • Incorporating Side Information by Adaptive Convolution
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-07-02
    Di Kang, Debarun Dhar, Antoni B. Chan

    Computer vision tasks often have side information available that is helpful to solve the task. For example, for crowd counting, the camera perspective (e.g., camera angle and height) gives a clue about the appearance and scale of people in the scene. While side information has been shown to be useful for counting systems using traditional hand-crafted features, it has not been fully utilized in deep

    更新日期:2020-07-02
  • Inferring 3D Shapes from Image Collections Using Adversarial Networks
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-06-24
    Matheus Gadelha, Aartika Rai, Subhransu Maji, Rui Wang

    We investigate the problem of learning a probabilistic distribution over three-dimensional shapes given two-dimensional views of multiple objects taken from unknown viewpoints. Our approach called projective generative adversarial network (PrGAN) trains a deep generative model of 3D shapes whose projections (or renderings) matches the distribution of the provided 2D views. The addition of a differentiable

    更新日期:2020-06-24
  • Mix and Match Networks: Cross-Modal Alignment for Zero-Pair Image-to-Image Translation
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-06-15
    Yaxing Wang, Luis Herranz, Joost van de Weijer

    This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way

    更新日期:2020-06-15
  • Hardware-Centric AutoML for Mixed-Precision Quantization
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-06-11
    Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han

    Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support flexible bitwidth (1–8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off accuracy, latency

    更新日期:2020-06-11
  • SliderGAN: Synthesizing Expressive Face Images by Sliding 3D Blendshape Parameters
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-06-11
    Evangelos Ververas, Stefanos Zafeiriou

    Image-to-image (i2i) translation is the dense regression problem of learning how to transform an input image into an output using aligned image pairs. Remarkable progress has been made in i2i translation with the advent of deep convolutional neural networks and particular using the learning paradigm of generative adversarial networks (GANs). In the absence of paired images, i2i translation is tackled

    更新日期:2020-06-11
  • Gradient Shape Model
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-06-09
    Pedro Martins, João F. Henriques, Jorge Batista

    For years, the so-called Constrained Local Model (CLM) and its variants have been the gold standard in face alignment tasks. The CLM combines an ensemble of local feature detectors whose locations are regularized by a shape model. Fitting such a model typically consists of an exhaustive local search using the detectors and a global optimization that finds the CLM’s parameters that jointly maximize

    更新日期:2020-06-09
  • Learning the Clustering of Longitudinal Shape Data Sets into a Mixture of Independent or Branching Trajectories
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-06-03
    Vianney Debavelaere, Stanley Durrleman, Stéphanie Allassonnière

    Given repeated observations of several subjects over time, i.e. a longitudinal data set, this paper introduces a new model to learn a classification of the shapes progression in an unsupervised setting: we automatically cluster a longitudinal data set in different classes without labels. Our method learns for each cluster an average shape trajectory (or representative curve) and its variance in space

    更新日期:2020-06-03
  • Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-06-03
    Aoxue Li, Zhiwu Lu, Jiechao Guan, Tao Xiang, Liwei Wang, Ji-Rong Wen

    Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by learning a projection function between a feature space and a semantic space (e.g. attribute space). Considering the seen and unseen classes as two domains, a big domain gap often exists which challenges ZSL. In this work, we

    更新日期:2020-06-03
  • Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-05-30
    Ke Li, Shichong Peng, Tianhao Zhang, Jitendra Malik

    Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets have delivered impressive advances in quality of synthesized images. However, it remains a challenge to generate both diverse and plausible images for the same input, due to the problem of mode collapse. In this paper, we develop a new generic multimodal

    更新日期:2020-05-30
  • Train Sparsely, Generate Densely: Memory-Efficient Unsupervised Training of High-Resolution Temporal GAN
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-05-29
    Masaki Saito, Shunta Saito, Masanori Koyama, Sosuke Kobayashi

    Training of generative adversarial network (GAN) on a video dataset is a challenge because of the sheer size of the dataset and the complexity of each observation. In general, the computational cost of training GAN scales exponentially with the resolution. In this study, we present a novel memory efficient method of unsupervised learning of high-resolution video dataset whose computational cost scales

    更新日期:2020-05-29
  • Compositional GAN: Learning Image-Conditional Binary Composition
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-05-28
    Samaneh Azadi, Deepak Pathak, Sayna Ebrahimi, Trevor Darrell

    Generative Adversarial Networks can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint

    更新日期:2020-05-28
  • High-Quality Video Generation from Static Structural Annotations
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-05-28
    Lu Sheng, Junting Pan, Jiaming Guo, Jing Shao, Chen Change Loy

    This paper proposes a novel unsupervised video generation that is conditioned on a single structural annotation map, which in contrast to prior conditioned video generation approaches, provides a good balance between motion flexibility and visual quality in the generation process. Different from end-to-end approaches that model the scene appearance and dynamics in a single shot, we try to decompose

    更新日期:2020-05-28
  • Weakly-supervised Semantic Guided Hashing for Social Image Retrieval
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-05-12
    Zechao Li, Jinhui Tang, Liyan Zhang, Jian Yang

    Hashing has been widely investigated for large-scale image retrieval due to its search effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided Hashing method coupled with binary matrix factorization to perform more effective nearest neighbor image search by simultaneously exploring the weakly-supervised rich community-contributed information and the underlying data

    更新日期:2020-05-12
  • 3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-05-06
    Stylianos Moschoglou, Stylianos Ploumpis, Mihalis A. Nicolaou, Athanasios Papaioannou, Stefanos Zafeiriou

    Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can successfully represent, generate or translate 3D facial shapes (meshes).

    更新日期:2020-05-06
  • Hadamard Matrix Guided Online Hashing
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-05-06
    Mingbao Lin, Rongrong Ji, Hong Liu, Xiaoshuai Sun, Shen Chen, Qi Tian

    Online image hashing has attracted increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly. Its key challenge lies in the difficulty of balancing the learning timeliness and model accuracy. To this end, most works follow a supervised setting, i.e., using class labels to boost the hashing performance, which defects in two

    更新日期:2020-05-06
  • Towards Image-to-Video Translation: A Structure-Aware Approach via Multi-stage Generative Adversarial Networks
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-04-28
    Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas

    In this paper, we consider the problem of image-to-video translation, where one or a set of input images are translated into an output video which contains motions of a single object. Especially, we focus on predicting motions conditioned by high-level structures, such as facial expression and human pose. Recent approaches are either driven by structural conditions or temporal-based. Condition-driven

    更新日期:2020-04-28
  • DGPose: Deep Generative Models for Human Body Analysis
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-04-24
    Rodrigo de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip Torr

    Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in which the body pose and the visual appearance are disentangled. Such a disentanglement allows independent

    更新日期:2020-04-24
  • Product Quantization Network for Fast Visual Search
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-04-23
    Tan Yu , Jingjing Meng, Chen Fang, Hailin Jin, Junsong Yuan

    Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features. By constructing the approximation function, we extend the hard-assignment quantization to soft-assignment quantization. Thanks to the differentiable property of the soft-assignment quantization, the product quantization operation can be integrated as a layer in a convolutional

    更新日期:2020-04-23
  • A General Framework for Deep Supervised Discrete Hashing
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-04-18
    Qi Li, Zhenan Sun, Ran He, Tieniu Tan

    With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefiting from recent advances in deep learning, deep hashing methods have shown superior performance over the traditional hashing methods. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited)

    更新日期:2020-04-22
  • Necessary and Sufficient Polynomial Constraints on Compatible Triplets of Essential Matrices
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-04-18
    E. V. Martyushev

    The essential matrix incorporates relative rotation and translation parameters of two calibrated cameras. The well-known algebraic characterization of essential matrices, i.e. necessary and sufficient conditions under which an arbitrary matrix (of rank two) becomes essential, consists of a single matrix equation of degree three. Based on this equation, a number of efficient algorithmic solutions to

    更新日期:2020-04-22
  • Correction to: KS(conf): A Light-Weight Test if a Multiclass Classifier Operates Outside of Its Specifications
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2019-11-09
    Rémy Sun, Christoph H. Lampert

    The original version of this article contained a mistake in the denominator of equation (1).

    更新日期:2020-04-22
  • KS(conf): A Light-Weight Test if a Multiclass Classifier Operates Outside of Its Specifications.
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2019-10-10
    Rémy Sun,Christoph H Lampert

    We study the problem of automatically detecting if a given multi-class classifier operates outside of its specifications (out-of-specs), i.e. on input data from a different distribution than what it was trained for. This is an important problem to solve on the road towards creating reliable computer vision systems for real-world applications, because the quality of a classifier's predictions cannot

    更新日期:2020-04-22
  • End-to-End Learning of Decision Trees and Forests
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2019-10-09
    Thomas M. Hehn, Julian F. P. Kooij, Fred A. Hamprecht

    Conventional decision trees have a number of favorable properties, including a small computational footprint, interpretability, and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable. Kontschieder et al. (ICCV, 2015) have addressed this deficit, but at the cost of losing a main attractive

    更新日期:2020-04-22
  • Simultaneous Deep Stereo Matching and Dehazing with Feature Attention
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-01-21
    Taeyong Song, Youngjung Kim, Changjae Oh, Hyunsung Jang, Namkoo Ha, Kwanghoon Sohn

    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 clear image

    更新日期:2020-04-22
  • Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-26
    James Pritts, Zuzana Kukelova, Viktor Larsson, Yaroslava Lochman, Ondřej Chum

    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 scale in

    更新日期:2020-04-22
  • EdgeStereo: An Effective Multi-task Learning Network for Stereo Matching and Edge Detection
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-01-28
    Xiao Song, Xu Zhao, Liangji Fang, Hanwen Hu, Yizhou Yu

    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 by

    更新日期:2020-04-22
  • Learning on the Edge: Investigating Boundary Filters in CNNs
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2019-10-08
    Carlo Innamorati, Tobias Ritschel, Tim Weyrich, Niloy J. Mitra

    Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that

    更新日期:2020-04-22
  • 3D Fluid Flow Estimation with Integrated Particle Reconstruction
    Int. J. Comput. Vis. (IF 5.698) 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:2020-04-22
  • Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2019-10-16
    Paul Henderson, Vittorio Ferrari

    We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general:

    更新日期:2020-04-22
  • Modeling Human Motion with Quaternion-Based Neural Networks
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2019-10-08
    Dario Pavllo, Christoph Feichtenhofer, Michael Auli, David Grangier

    Previous work on predicting or generating 3D human pose sequences regresses either joint rotations or joint positions. The former strategy is prone to error accumulation along the kinematic chain, as well as discontinuities when using Euler angles or exponential maps as parameterizations. The latter requires re-projection onto skeleton constraints to avoid bone stretching and invalid configurations

    更新日期:2020-04-22
  • A Simple and Light-Weight Attention Module for Convolutional Neural Networks
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-01-28
    Jongchan Park, Sanghyun Woo, Joon-Young Lee, In So Kweon

    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 along two

    更新日期:2020-04-22
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