Mixup-CLIPood: Robust Domain Generalization for Multi-modal Object Recognition
Yuxin Qiao, Keqin Li, Junhong Lin, Rong Wei, Chufeng Jiang, Yang Luo, Haoyu Yang.

• Contribution
  • Address the incongruity between the actual loss and the one documented, and we deduce the actual loss used.
  • Expand the experiments to encompass two larger vision-language backbones.
  • Propose Mixup-CLIPood with a novel mix-up loss to enhance the previous model’s generalization ability.

Large language models for forecasting and anomaly detection: A systematic literature review
Jing Su, Chufeng Jiang, Xin Jin, Yuxin Qiao, Tingsong Xiao, Hongda Ma, Rong Wei, Zhi Jing, Jiajun Xu, Junhong Lin

• Research Questions
  • Q1: What methodologies are employed in LLMs for forecasting in different domains?
  • Q2: How effective are LLMs in detecting anomalies compared to traditional anomaly detection methods?
  • Q3: What are the limitations and challenges of using LLMs for forecasting and anomaly detection?
• Academic Impact:
  • This paper is selected to present in a Collaborative Study and Research Group on LLM Literature 时序时空大模型读书会启动：大模型开启时序时空数据挖掘新视角.