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To Adapt or Not to Adapt? Selective Adaptation for Vision-Language Models
Siru Jiang, Yuwei Liang, Jian Liang +2
Test-time adaptation (TTA) has emerged as a prominent strategy for adapting vision-language models to distribution shifts during inference. We conduct a per-sample analysis of mode…
USE: A Unified Self-Ensembling Framework for Test-Time Prompt Tuning
Siru Jiang, Jian Liang, Ran He +1
Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of vision-language models (e.g., CLIP) on downstream tasks. Among existing CLIP-based TTA…
Cooperative Pseudo Labeling for Unsupervised Federated Classification
Kuangpu Guo, Lijun Sheng, Yongcan Yu +3
Unsupervised Federated Learning (UFL) aims to collaboratively train a global model across distributed clients without sharing data or accessing label information. Previous UFL work…
Towards Compatible Fine-tuning for Vision-Language Model Updates
Zhengbo Wang, Jian Liang, Lijun Sheng +3
So far, efficient fine-tuning has become a popular strategy for enhancing the capabilities of foundation models on downstream tasks by learning plug-and-play modules. However, exis…
A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
Zhengbo Wang, Jian Liang, Lijun Sheng +3
Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods,…