3 citations · 4 across the 4 of their papers we have counts for
4 papers
AmPLe: Supporting Vision-Language Models via Adaptive-Debiased Ensemble Multi-Prompt Learning
Fei Song, Yi Li, Jiangmeng Li +4
Multi-prompt learning methods have emerged as an effective approach for facilitating the rapid adaptation of vision-language models to downstream tasks with limited resources. Exis…
Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models
Fei Song, Yi Li, Rui Wang +3
Test-time prompt tuning for vision-language models has demonstrated impressive generalization capabilities under zero-shot settings. However, tuning the learnable prompts solely ba…
Revisiting Communication Efficiency in Multi-Agent Reinforcement Learning from the Dimensional Analysis Perspective
Chuxiong Sun, Peng He, Rui Wang +1
In this work, we introduce a novel perspective, i.e., dimensional analysis, to address the challenge of communication efficiency in Multi-Agent Reinforcement Learning (MARL). Our f…
Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective
Qirui Ji, Jiangmeng Li, Jie Hu +3
Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the str…