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Hao Tang - IEEE Xplore Author Profile

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Author details

Hao Tang

Publications

77

Citations

2,711

Publications by Year

20182025

Co-Authors:

Xavier Alameda-PinedaNarisu BaiBing-Kun BaoLorenzo BruzzoneDawen Cai

Show All Co-Authors (202)

Hao Tang

Affiliation

School of Computer Science

Peking University

Beijing, China

Publication Topics

Generative Adversarial Networks,
Convolutional Layers,
Convolutional Neural Network,
Deep Neural Network,
Feature Maps,
Discriminative Features,
Vision Transformer,
Attention Mechanism,
Attention Module,
Fréchet Inception Distance,
Latent Space,
Extensive Experiments

Biography

Hao Tang received the master’s degree from the School of Electronics and Computer Engineering, Peking University, Beijing, China, in 2016, and the Ph.D. degree from the Multimedia and Human Understanding Group, University of Trento, Trento, Italy, in 2021.,He was a Visiting Scholar with the Department of Engineering Science, University of Oxford, Oxford, U.K. He currently holds a post-doctoral position with the Computer Vision Laboratory, ETH Zürich, Zürich, Switzerland. His research interests are deep learning, machine learning, and their applications to computer vision.(Based on document published on 18 April 2022).

Publications

77

Citations

2,711

Publications by Year

20182025

Co-Authors:

Xavier Alameda-Pineda
Narisu Bai
Bing-Kun Bao
Lorenzo Bruzzone
Dawen Cai

Show All Co-Authors (202)

Author's Published Works

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Showing 1-25 of 77 results

Conferences (45)

Journals (30)

Early Access Articles (2)

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Hao Tang(77)
Nicu Sebe(26)
Yan Yan(14)
Luc Van Gool(10)
Xiaohui Xie(10)
Department of Information Engineering and Computer Science (DISI), University of Trento, Trento, Italy(12)
Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland(12)
Department of Information Engineering and Computer Science, University of Trento, Trento, Italy(9)
Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland(7)
University of Trento(6)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(6)
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(6)
IEEE Transactions on Geoscience and Remote Sensing(6)
IEEE Transactions on Image Processing(5)
IEEE Transactions on Multimedia(5)
IEEE(77)
Media(3)
Vancouver, BC, Canada(7)
Waikoloa, HI, USA(7)
New Orleans, LA, USA(6)
Montreal, QC, Canada(4)
Bordeaux, France(2)
Generative Adversarial Networks(28)
Convolutional Neural Network(26)
Feature Maps(24)
Convolutional Layers(18)
Attention Mechanism(14)

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Hierarchical Cross-Attention Network for Virtual Try-On

Hao Tang;Bin Ren;Pingping Wu;Nicu Sebe

IEEE Transactions on Multimedia

Year: 2025 | Early Access Article |

In this paper, we present an innovative solution tailored for the intricate challenges of the virtual try-on task–our novel Hierarchical Cross-Attention Network, HCANet. HCANet is meticulously crafted with two primary stages: geometric matching and try-on, each playing a crucial role in delivering realistic and visually convincing virtual try-on outcomes. A distinctive feature of HCANet is the inc...Show More

Hierarchical Cross-Attention Network for Virtual Try-On

Hao Tang;Bin Ren;Pingping Wu;Nicu Sebe

IEEE Transactions on Multimedia

Year: 2025 | Early Access Article |

Hierarchical Distribution-Based Exemplar Replay for Incremental SAR Automatic Target Recognition

Haohao Ren;Rongsheng Zhou;Lin Zou;Hao Tang

IEEE Transactions on Aerospace and Electronic Systems

Year: 2025 | Early Access Article |

Over the years, deep learning-based automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery has made remarkable progress on the assumption that the target category library is immutable. However, the target category library will continue to expand over time in real-world scenarios, so the ATR model should be updated to acquire reasoning capabilities for subsequent acquired targ...Show More

Hierarchical Distribution-Based Exemplar Replay for Incremental SAR Automatic Target Recognition

Haohao Ren;Rongsheng Zhou;Lin Zou;Hao Tang

IEEE Transactions on Aerospace and Electronic Systems

Year: 2025 | Early Access Article |

TSLA: A Task-Specific Learning Adaptation for Semantic Segmentation on Autonomous Vehicles Platform

Jun Liu;Zhenglun Kong;Pu Zhao;Weihao Zeng;Hao Tang;Xuan Shen;Changdi Yang;Wenbin Zhang;Geng Yuan;Wei Niu;Xue Lin;Yanzhi Wang

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Year: 2025 | Volume: 44, Issue: 4 | Journal Article |

Cited by: Papers (1)

Autonomous driving platforms encounter diverse driving scenarios, each with varying hardware resources and precision requirements. Given the computational limitations of embedded devices, it is crucial to consider computing costs when deploying on target platforms like the DRIVE PX 2. Our objective is to customize the semantic segmentation network according to the computing power and specific scen...Show More

TSLA: A Task-Specific Learning Adaptation for Semantic Segmentation on Autonomous Vehicles Platform

Jun Liu;Zhenglun Kong;Pu Zhao;Weihao Zeng;Hao Tang;Xuan Shen;Changdi Yang;Wenbin Zhang;Geng Yuan;Wei Niu;Xue Lin;Yanzhi Wang

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Year: 2025 | Volume: 44, Issue: 4 | Journal Article |

HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud

Wencan Cheng;Hao Tang;Luc Van Gool;Jong Hwan Ko

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications. Essentially, the 3D hand pose estimation can be regarded as a 3D point subset generative problem conditioned on input frames. Thanks to the recent significant progress on diffusion-based generative models, hand pose estimation can also benef...Show More

HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud

Wencan Cheng;Hao Tang;Luc Van Gool;Jong Hwan Ko

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Physical Adversarial Attack Meets Computer Vision: A Decade Survey

Hui Wei;Hao Tang;Xuemei Jia;Zhixiang Wang;Hanxun Yu;Zhubo Li;Shin’ichi Satoh;Luc Van Gool;Zheng Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

Cited by: Papers (6)

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Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs’ performance. This perplexing phenomenon not only exists in the digital space but also in the ...Show More

Physical Adversarial Attack Meets Computer Vision: A Decade Survey

Hui Wei;Hao Tang;Xuemei Jia;Zhixiang Wang;Hanxun Yu;Zhubo Li;Shin’ichi Satoh;Luc Van Gool;Zheng Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

Hybrid Reasoning Network With Class-Oriented Hierarchical Representation for Few-Shot SAR Target Recognition

Haohao Ren;Sen Liu;Lei Miao;Xuelian Yu;Lin Zou;Yun Zhou;Hao Tang

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 16 | Journal Article |

Cited by: Papers (1)

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The rise of deep learning has furnished a potent boost for the rapid development of automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery. The existing SAR ATR methods can achieve impressive results with the great many labeled samples available. However, in real SAR application scenarios, the acquisition of quite a few SAR samples is costly or sometimes infeasible. Thus, SAR...Show More

Hybrid Reasoning Network With Class-Oriented Hierarchical Representation for Few-Shot SAR Target Recognition

Haohao Ren;Sen Liu;Lei Miao;Xuelian Yu;Lin Zou;Yun Zhou;Hao Tang

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 16 | Journal Article |

SKIP: Accurate Fall Detection Based on Skeleton Keypoint Association and Critical Feature Perception

Chenjie Du;Ran Jin;Hao Tang;Qiuping Jiang;Zhiwei He

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 9 | Journal Article |

Cited by: Papers (1)

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As deep learning technology advances, human fall detection (HFD) leveraging convolutional neural networks (CNNs) has recently garnered significant interest within the research community. However, most existing works ignore the cross-frame association of skeleton keypoints and aggregation of feature representations. To address this, we first introduce an image preprocessing (IPP) module, which enha...Show More

SKIP: Accurate Fall Detection Based on Skeleton Keypoint Association and Critical Feature Perception

Chenjie Du;Ran Jin;Hao Tang;Qiuping Jiang;Zhiwei He

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 9 | Journal Article |

Adapting Segment Anything Model for Change Detection in VHR Remote Sensing Images

Lei Ding;Kun Zhu;Daifeng Peng;Hao Tang;Kuiwu Yang;Lorenzo Bruzzone

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (30)

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Vision foundation models (VFMs), such as the segment anything model (SAM), allow zero-shot or interactive segmentation of visual contents; thus, they are quickly applied in a variety of visual scenes. However, their direct use in many remote sensing (RS) applications is often unsatisfactory due to the special imaging properties of RS images (RSIs). In this work, we aim to utilize the strong visual...Show More

Adapting Segment Anything Model for Change Detection in VHR Remote Sensing Images

Lei Ding;Kun Zhu;Daifeng Peng;Hao Tang;Kuiwu Yang;Lorenzo Bruzzone

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Graph Transformer GANs With Graph Masked Modeling for Architectural Layout Generation

Hao Tang;Ling Shao;Nicu Sebe;Luc Van Gool

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 6 | Journal Article |

Cited by: Papers (2)

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We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for challenging graph-constrained architectural layout generation tasks. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and gl...Show More

Graph Transformer GANs With Graph Masked Modeling for Architectural Layout Generation

Hao Tang;Ling Shao;Nicu Sebe;Luc Van Gool

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 6 | Journal Article |

Toward High-Quality HDR Deghosting With Conditional Diffusion Models

Qingsen Yan;Tao Hu;Yuan Sun;Hao Tang;Yu Zhu;Wei Dong;Luc Van Gool;Yanning Zhang

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 5 | Journal Article |

Cited by: Papers (32)

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High Dynamic Range (HDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. Despite the remarkable progress, DNN-based methods still generate ghosting artifacts when LDR images have saturation and large motion, which hinders potential applications in real-world scenarios. To address this challenge, we formulate the HDR deghosting...Show More

Toward High-Quality HDR Deghosting With Conditional Diffusion Models

Qingsen Yan;Tao Hu;Yuan Sun;Hao Tang;Yu Zhu;Wei Dong;Luc Van Gool;Yanning Zhang

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 5 | Journal Article |

UniTR: A Unified and Efficient Multi-Modal Transformer for Bird’s-Eye-View Representation

Haiyang Wang;Hao Tang;Shaoshuai Shi;Aoxue Li;Zhenguo Li;Bernt Schiele;Liwei Wang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (23)

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Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems. However, current 3D perception research follows a modality-specific paradigm, leading to additional computation overheads and inefficient collaboration between different sensor data. In this paper, we present an efficient multi-modal backbone for outd...Show More

UniTR: A Unified and Efficient Multi-Modal Transformer for Bird’s-Eye-View Representation

Haiyang Wang;Hao Tang;Shaoshuai Shi;Aoxue Li;Zhenguo Li;Bernt Schiele;Liwei Wang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Learning Concordant Attention via Target-aware Alignment for Visible-Infrared Person Re-identification

Jianbing Wu;Hong Liu;Yuxin Su;Wei Shi;Hao Tang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (10)

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Owing to the large distribution gap between the heterogeneous data in Visible-Infrared Person Re-identification (VI Re-ID), we point out that existing paradigms often suffer from the inter-modal semantic misalignment issue and thus fail to align and compare local details properly. In this paper, we present Concordant Attention Learning (CAL), a novel framework that learns semantic-aligned represen...Show More

Learning Concordant Attention via Target-aware Alignment for Visible-Infrared Person Re-identification

Jianbing Wu;Hong Liu;Yuxin Su;Wei Shi;Hao Tang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Graph Transformer GANs for Graph-Constrained House Generation

Hao Tang;Zhenyu Zhang;Humphrey Shi;Bo Li;Ling Shao;Nicu Sebe;Radu Timofte;Luc Van Gool

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (10)

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We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interac...Show More

Graph Transformer GANs for Graph-Constrained House Generation

Hao Tang;Zhenyu Zhang;Humphrey Shi;Bo Li;Ling Shao;Nicu Sebe;Radu Timofte;Luc Van Gool

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders

Qingsen Yan;Song Zhang;Weiye Chen;Hao Tang;Yu Zhu;Jinqiu Sun;Luc Van Gool;Yanning Zhang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (7)

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Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generate satisfactory images with limited data. However, it is difficult for modern DNN...Show More

SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders

Qingsen Yan;Song Zhang;Weiye Chen;Hao Tang;Yu Zhu;Jinqiu Sun;Luc Van Gool;Yanning Zhang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Pruning Parameterization with Bi-level Optimization for Efficient Semantic Segmentation on the Edge

Changdi Yang;Pu Zhao;Yanyu Li;Wei Niu;Jiexiong Guan;Hao Tang;Minghai Qin;Bin Ren;Xue Lin;Yanzhi Wang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (8)

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With the ever-increasing popularity of edge devices, it is necessary to implement real-time segmentation on the edge for autonomous driving and many other applications. Vision Transformers (ViTs) have shown considerably stronger results for many vision tasks. However, ViTs with the fullattention mechanism usually consume a large number of computational resources, leading to difficulties for real- ...Show More

Pruning Parameterization with Bi-level Optimization for Efficient Semantic Segmentation on the Edge

Changdi Yang;Pu Zhao;Yanyu Li;Wei Niu;Jiexiong Guan;Hao Tang;Minghai Qin;Bin Ren;Xue Lin;Yanzhi Wang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis

Ming Tao;Bing-Kun Bao;Hao Tang;Changsheng Xu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (62)

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Synthesizing high-fidelity complex images from text is challenging. Based on large pretraining, the autoregressive and diffusion models can synthesize photo-realistic images. Although these large models have shown notable progress, there remain three flaws. 1) These models require tremendous training data and parameters to achieve good performance. 2) The multi-step generation design slows the ima...Show More

GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis

Ming Tao;Bing-Kun Bao;Hao Tang;Changsheng Xu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

Xuan Shen;Yaohua Wang;Ming Lin;Yilun Huang;Hao Tang;Xiuyu Sun;Yanzhi Wang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (11)

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The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN models can achieve as good performance as ViT models when carefully tuned. While encouraging, designing such high-performance CNN models is challengi...Show More

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

Xuan Shen;Yaohua Wang;Ming Lin;Yilun Huang;Hao Tang;Xiuyu Sun;Yanzhi Wang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer

Hao Tang;Songhua Liu;Tianwei Lin;Shaoli Huang;Fu Li;Dongliang He;Xinchao Wang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (5)

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Transformer-based models achieve favorable performance in artistic style transfer recently thanks to its global receptive field and powerful multi-head/layer attention operations. Nevertheless, the over-paramerized multi-layer structure increases parameters significantly and thus presents a heavy burden for training. Moreover, for the task of style transfer, vanilla Transformer that fuses content ...Show More

Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer

Hao Tang;Songhua Liu;Tianwei Lin;Shaoli Huang;Fu Li;Dongliang He;Xinchao Wang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

LSDIR: A Large Scale Dataset for Image Restoration

Yawei Li;Kai Zhang;Jingyun Liang;Jiezhang Cao;Ce Liu;Rui Gong;Yulun Zhang;Hao Tang;Yun Liu;Denis Demandolx;Rakesh Ranjan;Radu Timofte;Luc Van Gool

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Cited by: Papers (37)

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The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networks ‘only’ contain some thousands of images, which is still incomparable with the large scale datasets for other vision tasks such as visual recognition and object detection. The small...Show More

LSDIR: A Large Scale Dataset for Image Restoration

Yawei Li;Kai Zhang;Jingyun Liang;Jiezhang Cao;Ce Liu;Rui Gong;Yulun Zhang;Hao Tang;Yun Liu;Denis Demandolx;Rakesh Ranjan;Radu Timofte;Luc Van Gool

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Edge Guided GANs With Multi-Scale Contrastive Learning for Semantic Image Synthesis

Hao Tang;Guolei Sun;Nicu Sebe;Luc Van Gool

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 12 | Journal Article |

Cited by: Papers (7)

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We propose a novel edge guided generative adversarial network with contrastive learning (ECGAN) for the challenging semantic image synthesis task. Although considerable improvements have been achieved by the community in the recent period, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural...Show More

Edge Guided GANs With Multi-Scale Contrastive Learning for Semantic Image Synthesis

Hao Tang;Guolei Sun;Nicu Sebe;Luc Van Gool

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2023 | Volume: 45, Issue: 12 | Journal Article |

Adaptive Convolutional Subspace Reasoning Network for Few-Shot SAR Target Recognition

Haohao Ren;Xuelian Yu;Sen Liu;Lin Zou;Xuegang Wang;Hao Tang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Cited by: Papers (11)

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Data-driven automatic target recognition (ATR) methods have become the mainstream in the synthetic aperture radar (SAR) community at this stage. However, in real SAR application scenarios, the scarcity of training samples is a common problem. Especially in military application scenarios, only a small number of samples of each type of target are usually available. In the case of limited sample avai...Show More

Adaptive Convolutional Subspace Reasoning Network for Few-Shot SAR Target Recognition

Haohao Ren;Xuelian Yu;Sen Liu;Lin Zou;Xuegang Wang;Hao Tang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Measuring the Consistency and Diversity of 3D Face Generation

Kunlin Liu;Wenbo Zhou;Zhenyu Zhang;Yanhao Ge;Hao Tang;Weiming Zhang;Nenghai Yu

IEEE Journal of Selected Topics in Signal Processing

Year: 2023 | Volume: 17, Issue: 6 | Journal Article |

Cited by: Papers (1)

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3D-aware GANs have shown their impressive power on 3D controlling for synthesized portraits. While the plausible facial reality is achieved, the inherent 3D properties of the generated results have actually not been well analyzed. One of the reasons is that the wildly-used metrics, such as Inception Score (IS) or Fréchet Inception Distance (FID), focus more on the perceptual features rather than e...Show More

Measuring the Consistency and Diversity of 3D Face Generation

Kunlin Liu;Wenbo Zhou;Zhenyu Zhang;Yanhao Ge;Hao Tang;Weiming Zhang;Nenghai Yu

IEEE Journal of Selected Topics in Signal Processing

Year: 2023 | Volume: 17, Issue: 6 | Journal Article |

PI-Trans: Parallel-Convmlp and Implicit-Transformation Based Gan for Cross-View Image Translation

Bin Ren;Hao Tang;Yiming Wang;Xia Li;Wei Wang;Mcu Sebe

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is little overlap or drastic view difference between the source and target images. Hence, one not only needs to encode the long- range dependencies among pixels in both the source view i...Show More

PI-Trans: Parallel-Convmlp and Implicit-Transformation Based Gan for Cross-View Image Translation

Bin Ren;Hao Tang;Yiming Wang;Xia Li;Wei Wang;Mcu Sebe

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

MLP-GAN for Brain Vessel Image Segmentation

Bin Xie;Hao Tang;Bin Duan;Dawen Cai;Yan Yan

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Brain vessel image segmentation can be used as a promising biomarker for better prevention and treatment of different diseases. One successful approach is to consider the segmentation as an image-to-image translation task and perform a conditional Generative Adversarial Network (cGAN) to learn a transformation between two distributions. In this paper, we present a novel multi-view approach, MLP-GA...Show More

MLP-GAN for Brain Vessel Image Segmentation

Bin Xie;Hao Tang;Bin Duan;Dawen Cai;Yan Yan

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Transductive Prototypical Attention Reasoning Network for Few-Shot SAR Target Recognition

Haohao Ren;Sen Liu;Xuelian Yu;Lin Zou;Yun Zhou;Xuegang Wang;Hao Tang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Cited by: Papers (14)

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Deep-learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms have achieved outstanding performance under the condition of hundreds or thousands of training samples in recent years. Nevertheless, it is often rare to acquire great quantities of target samples in real SAR application scenarios. This article proposes a novel ATR method called transductive prototypic...Show More

Transductive Prototypical Attention Reasoning Network for Few-Shot SAR Target Recognition

Haohao Ren;Sen Liu;Xuelian Yu;Lin Zou;Yun Zhou;Xuegang Wang;Hao Tang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

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