Yeqi He(贺业祺)

Yeqi He(贺业祺)

Ph.D. Candidate, Intelligent Information Processing Laboratory(IIPL), Hangzhou Dianzi University. Jointly trained by the Visual Information Processing and Learning (VIPL) research group of the Institute of Computing Technology, Chinese Academy of Sciences

Publications

You can also find my articles on my Google Scholar profile.

Conference Papers

HAM: A Training-Free Style Transfer Approach via Heterogeneous Attention Modulation for Diffusion Models

Published in the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026)

  Diffusion models have demonstrated remarkable performance in image generation, particularly within the domain of style transfer. Prevailing style transfer approaches typically leverage pre-trained diffusion models’ robust feature extraction capabilities alongside external modular control pathways to explicitly impose style guidance signals. However, these methods often fail to capture complex style reference or retain the identity of user-provided content images, thus falling into the trap of style-content balance. Thus, we propose a training-free style transfer approach via heterogeneous attention modulation (HAM) to protect identity information during image/text-guided style reference transfer, thereby addressing the style-content trade-off challenge. Specifically, we first introduces style noise initialization to initialize latent noise for diffusion. Then, during the diffusion process, it innovatively employs HAM for different attention mechanisms, including Global Attention Regulation (GAR) and Local Attention Transplantation (LAT), which better preserving the details of the content image while capturing complex style references. Our approach is validated through a series of qualitative and quantitative experiments, achieving state-of-the-art performance on multiple quantitative metrics.

Cite: Yeqi He, Liang Li, Zhiwen Yang, Xichun Sheng, Zhidong Zhao, and Chenggang Yan. HAM: A Training-Free Style Transfer Approach via Heterogeneous Attention Modulation for Diffusion Models[J]. arXiv preprint arXiv:2603.24043, 2026.
Publisher | BibTeX

Journal Articles

Few-Shot Generative Model Adaption via Identity Injection and Preservation

Published in Arxiv, Submitted to ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2026

  Training generative models with limited data presents severe challenges of mode collapse. A common approach is to adapt a large pretrained generative model upon a target domain with very few samples (fewer than 10), known as few-shot generative model adaptation. However, existing methods often suffer from forgetting source domain identity knowledge during adaptation, which degrades the quality of generated images in the target domain. To address this, we propose Identity Injection and Preservation (I2P), which leverages identity injection and consistency alignment to preserve the source identity knowledge. Specifically, we first introduce an identity injection module that integrates source domain identity knowledge into the target domain’s latent space, ensuring the generated images retain key identity knowledge of the source domain. Second, we design an identity substitution module, which includes a style-content decoupler and a reconstruction modulator, to further enhance source domain identity preservation. We enforce identity consistency constraints by aligning features from identity substitution, thereby preserving identity knowledge. Both quantitative and qualitative experiments show that our method achieves substantial improvements over state-of-the-art methods on multiple public datasets and 5 metrics.

Cite: Yeqi He, Liang Li, Jiehua Zhang, Yaoqi Sun, Xichun Sheng, Zhidong Zhao, and Chenggang Yan. Few-Shot Generative Model Adaption via Identity Injection and Preservation[J]. arXiv preprint arXiv:2603.22965, 2026.
Publisher | BibTeX

Competition Essay

Checking the Pulse and Temperature of Higher Education

Published in Interdisciplinary Contest in Modeling (ICM), 2021

  Many countries in the world are paying increasing attention to the health of the higher education system which is related to the level of national development. Therefore, we build models for the health and sustainability of the higher education system and evaluate the level of higher education in each country and give effective recommendations.
  For Issue 1 and 2, we first collect ten important indexes that affect the evaluation of higher education systems in the United States, Canada, Switzerland, and China. They include, for example, the employment and unemployment rate of college students, and the proportion of the population with higher education. Then we conduct Analytic Hierarchy Process through the evaluation data of two experts and determine the hierarchical and weight relationship between the indexes. Finally, we calculate the final judgement result of each country through the Fuzzy Comprehensive Evaluation method. In 2019, the final judgement results of the United States, Canada, Switzerland and China are 0.7912, 0.7703, 0.7827, and 0.7545(see Figure 6).
  For Issue 3 and 4, we take China, who has the lowest final judgement result obtained in Issue 1 and 2, as our research object. By analyzing the weights and scores of the indexes, we select the proportion of the population in higher education(PPHE) and the research quality index(RQI) as indexes that need improvement. We collect their historical data and conduct Time Series Analysis. Then we use the Damped Trend Model to make future predictions for them. We use the 2020 Swiss final judgement result as the threshold. When China’s final judgement result reaches 0.784237 which exceeds the threshold, we select the PPHE 42.56% and RQI 61.72 for the year as the result and regard them as the vision for China’s higher education system.
  For Issue 5, we analyze the PPHE and RQI. Through the analysis of China’s national conditions, we decide to focus on adult higher education. We first predict the number of people that the new policy need to promote. Then we assess the strength of higher education of provinces in mainland China through the Analytic Hierarchy Process and get the number of people need to increase in each province. In the end, we analyze the 10-year policy arrangements for each province. We propose that universities divide the subjects into practical-based and research-based subjects. The classification of subjects not only makes application-oriented students more competitive after graduation, but also makes research-oriented students more capable of research.
  For Issue 6 and 7, we use the model in Issue 2 to evaluate the theoretical results of the new policy and get the final judgement result of China’s higher education system in the next ten years. The new policy brings the final judgement result to 0.8030 in 2030, which is greater than the one without the new policy and the effect is very significant.Finally, we discuss the impact and difficulties during policy implementation from two aspects: ownership of management rights and impact of market economy.

Cite: Yeqi He, Penghui Xue and Hongming Liu. Checking the Pulse and Temperature of Higher Education[J]. Interdisciplinary Contest in Modeling (ICM), 2021.
Publisher | BibTeX