I am pursuing a Ph.D. degree at Nanjing University under the supervision of Professor Qing Gu (顾庆) and Assistant Professor Zhiwei Jiang (蒋智威). Additionally, I am currently a visiting Ph.D. student at Singapore Management University (SMU), where I am guided by Associate Professor Qianru Sun and Assistant Professor Jiannan Li, with funding from the China Scholarship Council (CSC).

I have a broad interest in computer vision and deep learning, with a current focus on controllable and consistent generation in AIGC, including audio-driven video generation and text-to-image generation. My previous research experience encompasses various areas, such as essay scoring and ordinal classification.

From May 2023 to May 2024, I was a research intern at Tencent AI Lab, where I worked under the mentorship of Kuan Tian (田宽) and Jun Zhang (张军), concentrating on research in AIGC.

Research Experience

  • 2024.10Present, Visiting Ph.D. Student,
    School of Computing and Information Systems (SCIS), Singapore Management University (SMU), Singapore.
SMU
  • 2021.09Present, Ph.D. Student,
    School of Computer Science, Nanjing University (NJU), Nanjing, China.
NJU
  • 2023.052024.05, Research Intern,
    Tencent AI Lab, Technology & Engineering Group (TEG), Tencent, Shenzhen, China.
Tencent AI Lab

Honors and Awards

  • Outstanding Graduate Student, Nanjing University, 2023.
  • Huawei Scholarship, Nanjing University, 2023.
  • Yingcai Scholarship, Nanjing University, 2021.

Selected Publications

arXiv
sym

V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation;
Cong Wang*, Kuan Tian*, Jun Zhang, Yonghang Guan, Feng Luo, Fei Shen, Zhiwei Jiang, Qing Gu, Xiao Han, Wei Yang;
arXiv:2406.02511.
[code] [project page] [arXiv] [models]

TL;DR: V-Express aims to generate a talking head video under the control of a reference image, an audio, and a sequence of V-Kps images.

GitHub forks GitHub forks

tencent-ailab%2FV-Express | Trendshift

arXiv
sym

Ensembling Diffusion Models via Adaptive Feature Aggregation;
Cong Wang*, Kuan Tian*, Yonghang Guan, Jun Zhang, Zhiwei Jiang, Fei Shen, Xiao Han, Qing Gu, Wei Yang;
arXiv:2405.17082.
[code] [arXiv]

TL;DR: We propose Adaptive Feature Aggregation (AFA) to ensemble multiple diffusion models dynamically based on different states like prompts, noises, and spatial locations.

ACL 2023
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Aggregating Multiple Heuristic Signals as Supervision for Unsupervised Automated Essay Scoring;
Cong Wang, Zhiwei Jiang, Yafeng Yin, Zifeng Cheng, Shiping Ge, Qing Gu;
Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
[paper] [code] [poster] [slides] [video]

TL;DR: We propose ULRA for unsupervised automated essay scoring, which utilizes multiple heuristic quality signals to train a neural network using Deep Pairwise Rank Aggregation loss.

AAAI 2023
sym

Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning;
Cong Wang, Zhiwei Jiang, Yafeng Yin, Zifeng Cheng, Shiping Ge, Qing Gu;
AAAI Conference on Artificial Intelligence (AAAI), 2023.
[paper] [code] [poster] [slides] [arXiv]

TL;DR: We propose Constrained Proxies Learning for deep ordinal classification, which learns proxies for ordinal classes and adjusts their layout in feature space to capture ordinal relationships.

All Publications

Preprints

2024

  • AP-Adapter: Improving Generalization of Automatic Prompts on Unseen Text-to-Image Diffusion Models; Y. Fu, Z. Jiang, Y. Liu, C. Wang, Z. Deng, Z. Chen, Q. Gu; Annual Conference on Neural Information Processing Systems (NeurIPS).
  • Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models; F. Shen*, H. Ye*, J. Zhang, C. Wang, X. Han, W. Yang; International Conference on Learning Representations (ICLR).

2023

2022

* denotes equal contribution. denotes the corresponding author.

Academic Services

  • Journal Reviewer: TNNLS, TOMM;
  • Conference Reviewer: ICLR (25), ICIC (24), MM (23), EMNLP (23).