Yuhan Wang

Nanyang Technological University

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Jilin Li

China Academy of Space Technology

Ying Tai

Nanjing University

Feiyue Huang

First Affiliated Hospital of Chongqing Medical University

Shuzhou Yang

Peking University

Jiwen Yu

Peking University

Rui Tang

First Affiliated Hospital of Zhengzhou University

Yuanshuai Huang

Affiliated Hospital of Southwest Medical University

Mingfang Yang

Guizhou University

Jiliu Zhou

Chengdu University of Information Technology

Yinhuai Wang

Peking University

Cooperative Institutions

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Dalian University of Technology

Tencent (China)

Zhejiang University

Nanjing University of Science and Technology

Sichuan University

Peking University

University of Electronic Science and Technology of China

Nanyang Technological University

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Temporal-channel cascaded transformer for imagined handwriting character recognition

Neurocomputing (2024)

Wenhui Zhou Yuhan Wang Liangyan Mo Changsheng Li Mingyue Xu

Handwriting

10.1016/j.neucom.2024.127243

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Citations (0)

CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

Yuge Huang Yuhan Wang Ying Tai Xiaoming Liu Pengcheng Shen

As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recently, the idea of mining-based strategies is adopted to emphasize the misclassified samples, achieving promising results. However, during the entire training process, the prior methods either do not explicitly emphasize the sample based on its importance that renders the hard samples not fully exploited; or explicitly emphasize the effects of semi-hard/hard samples even at the early training stage that may lead to convergence issue. In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages. In each stage, different samples are assigned with different importance according to their corresponding difficultness. Extensive experimental results on popular benchmarks demonstrate the superiority of our CurricularFace over the state-of-the-art competitors.

Margin (machine learning)

Feature (linguistics)

Sample (material)

10.1109/cvpr42600.2020.00594

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Citations (443)

Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction

Lecture notes in computer science (2023)

Zeyu Han Yuhan Wang Luping Zhou Peng Wang Binyu Yan

Code (set theory)

10.1007/978-3-031-43999-5_23

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Citations (9)

The influences of ChatGPT on undergraduate students’ demonstrated and perceived interdisciplinary learning

Education and Information Technologies (2024)

Tianlong Zhong Gaoxia Zhu Chenyu Hou Yuhan Wang Xiuyi Fan

Electronic learning

10.1007/s10639-024-12787-9

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Citations (6)

HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping

Yuhan Wang Xu Chen Jun‐Wei Zhu Wenqing Chu Ying Tai

In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unlike other existing face swapping works that only use face recognition model to keep the identity similarity, we propose 3D shape-aware identity to control the face shape with the geometric supervision from 3DMM and 3D face reconstruction method. Meanwhile, we introduce the Semantic Facial Fusion module to optimize the combination of encoder and decoder features and make adaptive blending, which makes the results more photo-realistic. Extensive experiments on faces in the wild demonstrate that our method can preserve better identity, especially on the face shape, and can generate more photo-realistic results than previous state-of-the-art methods. Code is available at: https://johann.wang/HifiFace

Code (set theory)

Similarity (geometry)

10.24963/ijcai.2021/157

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Citations (81)

An Image Steganography Algorithm Based on Pixel Block Difference and Variable Modulus Function

Smart innovation, systems and technologies (2018)

Yu Yang Ru Zhang Jianyi Liu Yuhan Wang Fuhong Huang

10.1007/978-3-030-03748-2_5

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Citations (1)

Drawing Picture of Mental Disorder Patient and its Image Analysis: Using Texture Analysis, Fourier Analysis, and Singular Value Decomposition

Journal of Japan Society for Fuzzy Theory and Intelligent Informatics (2013)

Yumi Iwamitsu Kazuhisa Takemura Osamu Matsumura Yuhan Wang Asako Nobutou

精神医科学や臨床心理学においては，投映描画は個人の人格的理解と行動の全般的理解のために用いられている．特にコッホによって開発された樹木画（バウムテスト）は，医学的診断の補助や心理学的評価のために用いられることが多い．投映描画の解釈では客観性に乏しく，信頼性が低いため，われわれは，いくつかの画像解析技法の手法を統合した方法を用いて，精神障害患者の描画を解釈することを試みた．本研究で提案された方法は，下記のとおりである．（1）濃度ヒストグラム法（GLHM 法）,（2）空間濃度レベル依存法（SGLDM），（3）濃度レベル差分法（GLDM），（4）矩形行列を実行列または複素行列に分解する特異値分解，（5）画像の波形成分を分解するフーリエ解析，（6）画像解析をもとにした描画の臨床心理学的解釈である．これらの投映的樹木テストの画像解析諸技法は，精神障害の心理学的過程を解釈するために利用された．

Texture (cosmology)

Value (mathematics)

10.3156/jsoft.25.651

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Citations (3)

Swin-TCNet: Swin-based temporal-channel cascade network for motor imagery iEEG signal recognition

Biomedical Signal Processing and Control (2023)

Mingyue Xu Wenhui Zhou Xingfa Shen Yuhan Wang Liangyan Mo

Motor Imagery

10.1016/j.bspc.2023.104885

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Citations (3)

Image analysis of Tree test using Fourier transform

The Proceedings of the Annual Convention of the Japanese Psychological Association (2012)

Yuhan Wang Osamu Matsumura Yumi IWAMITU Kazuhisa Takemura

Tree (set theory)

10.4992/pacjpa.76.0_3pmc11

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Citations (0)

A Comparison of Content Based Image Retrieval Systems

Yuhan Wang Qiaochu Li Tian Lan James Chen

Content-based image retrieval (CBIR) is the application of computer vision techniques to the image retrieval problem. There are two main content-based image retrieval paradigms: one based on visual queries, referred to as query-by-visual-example (QBVE), and the other based on semantic content, denoted as semantic retrieval. In this paper, we compare these two kinds of retrieval systems by conducting experiments on two real typical content-based image retrieval systems: MUVIS and QBSE. The experiments show that QBSE has a better performance than MUVIS, which belongs to QBVE. Semantic space is the most important factor for QBSE system.

Content-Based Image Retrieval

Content (measure theory)

Factor (programming language)

10.1109/cse.2014.143

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Citations (3)