Combined Reinforcement Learning via Artificial Potential Field: A Case Study in Pommerman. - Yuqi Zhu; Shengze Li; Jieyuan Zhang; Xinhai Xu (ISCAS, 2021)

Efficient Searching With MCTS and Imitation Learning: A Case Study in Pommerman. - Hailan Yang; Shengze Li; Xinhai Xu; Xunyun Liu; Zhuxuan Meng; Yongjun Zhang 006 (IEEE Access, 2021)

Diverse Effective Relationship Exploration for Cooperative Multi-Agent Reinforcement Learning. - Hao Jiang 035; Yuntao Liu 004; Shengze Li; Jieyuan Zhang; Xinhai Xu; Donghong Liu (CIKM, 2022)

Exploring incomplete decoupling modeling with window and cross-window mechanism for skeleton-based action recognition. - Shengze Li; Xin Xiang; Jihong Fang; Jun Zhang 011; Songsong Cheng; Ke Wang (Knowl. Based Syst., 2023)

MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer. - Jianjian Cao; Peng Ye 006; Shengze Li; Chong Yu; Yansong Tang; Jiwen Lu; Tao Chen 003 (CVPR, 2024)

Generative subgoal oriented multi-agent reinforcement learning through potential field. - Shengze Li; Hao Jiang 035; Yuntao Liu 004; Jieyuan Zhang; Xinhai Xu; Donghong Liu (Neural Networks, 2024)

ClipSAM: CLIP and SAM collaboration for zero-shot anomaly segmentation. - Shengze Li; Jianjian Cao; Peng Ye 006; Yuhan Ding; Chongjun Tu; Tao Chen 003 (Neurocomputing, 2025)

Local global information aggregation graph convolution for skeleton-based action recognition. - Shichong Xie; Shengze Li; Peng Chen 001; Bing Wang 004; Jun Zhang 011 (Neurocomputing, 2025)