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  • 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
  • Pixelated Semantic Colorization
    Int. J. Comput. Vis. (IF 5.698) 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:2020-04-22
  • VOSTR: Video Object Segmentation via Transferable Representations
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-02-03
    Yi-Wen Chen, Yi-Hsuan Tsai, Yen-Yu Lin, Ming-Hsuan Yang

    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 in the

    更新日期:2020-04-22
  • Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in CNNs
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-04
    Timo Hackel, Mikhail Usvyatsov, Silvano Galliani, Jan D. Wegner, Konrad Schindler

    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 lend

    更新日期:2020-04-22
  • Learning Multi-human Optical Flow
    Int. J. Comput. Vis. (IF 5.698) 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:2020-04-22
  • Exploiting Semantics for Face Image Deblurring
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-30
    Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, Ming-Hsuan Yang

    In this paper, we propose an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks. As the human faces are highly structured and share unified facial components (e.g., eyes and mouths), such semantic information provides a strong prior for restoration. We incorporate face semantic labels as input priors and propose an adaptive structural

    更新日期:2020-03-30
  • Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-27
    Matthieu Grard, Emmanuel Dellandréa, Liming Chen

    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 of

    更新日期:2020-03-27
  • Enhanced Balanced Min Cut
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-26
    Xiaojun Chen, Weijun Hong, Feiping Nie, Joshua Zhexue Huang, Li Shen

    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 problem

    更新日期:2020-03-26
  • A Survey of Deep Facial Attribute Analysis
    Int. J. Comput. Vis. (IF 5.698) 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 5.698) 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 5.698) 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 5.698) 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
  • CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis
    Int. J. Comput. Vis. (IF 5.698) 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
  • Is There Anything New to Say About SIFT Matching?
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-17
    Fabio Bellavia, Carlo Colombo

    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:2020-03-17
  • Semi-online Multi-people Tracking by Re-identification
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-17
    Long Lan, Xinchao Wang, Gang Hua, Thomas S. Huang, Dacheng Tao

    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 a re-identification

    更新日期:2020-03-17
  • The Open Images Dataset V4
    Int. J. Comput. Vis. (IF 5.698) 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

    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 an initial

    更新日期:2020-03-13
  • Light Structure from Pin Motion: Geometric Point Light Source Calibration
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-13
    Hiroaki Santo, Michael Waechter, Wen-Yan Lin, Yusuke Sugano, Yasuyuki Matsushita

    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 and epipolar

    更新日期:2020-03-13
  • MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking
    Int. J. Comput. Vis. (IF 5.698) 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
  • Discriminator Feature-Based Inference by Recycling the Discriminator of GANs
    Int. J. Comput. Vis. (IF 5.698) 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
  • Convolutional Networks with Adaptive Inference Graphs
    Int. J. Comput. Vis. (IF 5.698) 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 5.698) 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
  • Deep Image Prior
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-04
    Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

    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 learning

    更新日期:2020-03-04
  • MAP Inference Via $$\ell _2$$ℓ2 -Sphere Linear Program Reformulation
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-03-04
    Baoyuan Wu, Li Shen, Tong Zhang, Bernard Ghanem

    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 relaxation

    更新日期:2020-03-04
  • Layout2image: Image Generation from Layout
    Int. J. Comput. Vis. (IF 5.698) 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 5.698) Pub Date : 2020-02-22
    Dimitrios Kollias, Shiyang Cheng, Evangelos Ververas, Irene Kotsia, Stefanos Zafeiriou

    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 facial

    更新日期:2020-02-22
  • Tensorized Multi-view Subspace Representation Learning
    Int. J. Comput. Vis. (IF 5.698) 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 5.698) 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
  • Unified Binary Generative Adversarial Network for Image Retrieval and Compression
    Int. J. Comput. Vis. (IF 5.698) 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
  • Multi-task Generative Adversarial Network for Detecting Small Objects in the Wild
    Int. J. Comput. Vis. (IF 5.698) Pub Date : 2020-02-18
    Yongqiang Zhang, Yancheng Bai, Mingli Ding, Bernard Ghanem

    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 benchmarks)

    更新日期:2020-02-18
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