Research
I'm mainly interested in multi/single-modal self-supervised representation learning (i.e., large model pre-training) and its downstream tasks in computer vision, i.e., few-shot learning, detection and segmentation.
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Focus Your Attention when Few-Shot Classification
Haoqing Wang, Shibo Jie, Zhi-Hong Deng
NeurIPS, 2023. CCF-A.
paper / code
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Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy
Shibo Jie, Haoqing Wang, Zhi-Hong Deng
ICCV, 2023. CCF-A.
paper / code
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Masked Image Modeling with Local Multi-Scale Reconstruction
Haoqing Wang, Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhi-Hong Deng, Kai Han
CVPR, 2023 (Highlight Presentation, Top 2.6%). CCF-A.
paper / code / blog
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Towards well-generalizing meta-learning via adversarial task augmentation
Haoqing Wang, Huiyu Mai, Yuhang Gong, Zhi-Hong Deng
Artificial Intelligence, 103875, 2023. CCF-A, IF=14.05.
paper / code
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Contrastive Prototypical Network with Wasserstein Confidence Penalty
Haoqing Wang, Zhi-Hong Deng
ECCV, 2022. CCF-B.
paper / code
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Rethinking minimal sufficient representation in contrastive learning
Haoqing Wang, Xun Guo, Zhi-Hong Deng, Yan Lu
CVPR, 2022 (Oral Presentation, Top 4.2%). CCF-A.
paper / code
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Cross-domain few-shot classification via adversarial task augmentation
Haoqing Wang, Zhi-Hong Deng
IJCAI, 2021. CCF-A.
paper / code / blog
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Distributed representations of diseases based on co-occurrence relationship
Haoqing Wang, Huiyu Mai, Zhi-Hong Deng, Chao Yang, Luxia Zhang, Huai-yu Wang,
Expert Systems with Applications 183, 115418, 2021. CCF-C, IF=8.665.
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Few-shot learning with LSSVM base learner and transductive modules
Haoqing Wang, Zhi-Hong Deng
arXiv preprint arXiv:2009.05786
paper / code
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Fast structured decoding for sequence models
Zhiqing Sun, Zhuohan Li, Haoqing Wang, Zi Lin, Di He, Zhi-Hong Deng
NeurIPS, 2019. CCF-A.
paper / code
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Internships
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Vision Intelligence Lab
2023/06/01-2024/02/19, advised by Kang Zhao
During the internship, I conduct deep research on video generation with diffusion models, especially on talking head generation.
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Algorithm Application Department
2022/06/15-2022/03/16, advised by Kai Han
During the internship, I conduct deep research on masked image modeling and propose a new pretext task, local multi-scale reconstruction, to accelerate representation learning. This work has been accepted by CVPR 2023 as Highlight/Oral presentation (Top 2.5%).
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Multimedia Search and Mining Group
2021/07/20-2022/04/11, advised by Xun Guo
During the internship, I conduct deep research on contrastive learning, revealing its shortcomings from a theoretical perspective and proposing solutions. This work has been accepted by CVPR 2022 as Oral presentation (Top 4.2%).
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Professional Services
1. Reviewer for CVPR, ICCV, NeurIPS, ICLR, ICML, ECCV, IJCV, TMM, ...
2. Editor of "Introduction to Health Data Science", responsible for "Chapter 9. Machine Learning".
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