Hierarchical Task-Incremental Learning with Feature-Space Initialization Inspired by Neural Collapse. - Qinhao Zhou; Xiang Xiang 001; Jing Ma (Neural Process. Lett., 2023)

Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer. - Yuwen Tan; Qinhao Zhou; Xiang Xiang 001; Ke Wang; Yuchuan Wu; Yongbin Li (CVPR, 2024)

Enhancing the General Agent Capabilities of Low-Paramter LLMs through Tuning and Multi-Branch Reasoning. - Qinhao Zhou; Zihan Zhang; Xiang Xiang 001; Ke Wang; Yuchuan Wu; Yongbin Li (NAACL-HLT (Findings), 2024)

Stay Open: Calibrating Weights Continuously for Detecting Out-of-Distribution Objects on the Fly. - Xiang Xiang 001; Qinhao Zhou (PRCV (12), 2024)

Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning. - Qinhao Zhou; Zihan Zhang; Xiang Xiang 001; Ke Wang; Yuchuan Wu; Yongbin Li (CoRR, 2024)

OpenEarthSensing: Large-Scale Fine-Grained Benchmark for Open-World Remote Sensing. - Xiang Xiang 001; Zhuo Xu; Yao Deng; Qinhao Zhou; Yifan Liang; Ke Chen 004; Qingfang Zheng; Yaowei Wang 001; Xi-Lin Chen 001; Wen Gao 001 (CoRR, 2025)

OpenHAIV: A Framework Towards Practical Open-World Learning. - Xiang Xiang 001; Qinhao Zhou; Zhuo Xu; Jing Ma; Jiaxin Dai; Yifan Liang; Hanlin Li (CoRR, 2025)

Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks. - Qinhao Zhou; Xiang Xiang 001; Kun He 001; John E. Hopcroft (CoRR, 2025)