Test-Time Adaptation for Zero-Shot Generalization of Large Vision Language Models
Imam, Raza
Imam, Raza
Author
Supervisor
Department
Machine Learning
Embargo End Date
2025-05-21
Type
Thesis
Date
2024
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
The conventional modus operandi for adapting pre-trained vision-language models (VLMs) during test-time involves tuning learnable prompts, i.e., test-time prompt tuning. This paper introduces Test-Time Low-rank adaptation (TTL) as an alternative to prompt tuning for zero-shot generalization of large-scale VLMs. Taking inspiration from recent advancements in efficiently fine-tuning large language models, TTL offers a test-time parameter-efficient adaptation approach that updates the attention weights of the transformer encoder by maximizing prediction confidence. The self-supervised confidence maximization objective is specified using a weighted entropy loss that enforces consistency among predictions of augmented samples. TTL introduces only a small amount of trainable parameters for low-rank adapters in the model space while keeping the prompts and backbone frozen. Extensive experiments on a variety of natural distribution and cross-domain tasks show that TTL can outperform other techniques for test-time optimization of VLMs in strict zero-shot settings. Specifically, TTL outperforms test-time prompt tuning baselines with a significant improvement on average. Our code and evaluation benchmark will be publicly released.
Citation
R. Imam, "Test-Time Adaptation for Zero-Shot Generalization of Large Vision Language Models", MS. Thesis, Machine Learning, MBZUAI, Abu Dhabi, UAE, 2024