Ultra-Light Test-Time Adaptation for Vision--Language Models

TL;DR

UL-TTA is a training-free, lightweight test-time adaptation method for vision-language models that improves accuracy and calibration under domain shift by adapting only logit-level parameters with Bayesian updates.

Contribution

It introduces UL-TTA, a fully training-free, backprop-free framework that adapts only logit-level parameters using Bayesian updates, suitable for streaming and edge scenarios.

Findings

Consistently improves accuracy on large-scale benchmarks.

Reduces calibration error significantly.

Operates with less than 8% latency overhead.

Abstract

Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot recognition by comparing image embeddings to text-derived class prototypes. However, under domain shift, they suffer from feature drift, class-prior mismatch, and severe miscalibration. Existing test-time adaptation (TTA) methods often require backpropagation through large backbones, covariance estimation, or heavy memory/state, which is problematic for streaming and edge scenarios. We propose Ultra-Light Test-Time Adaptation (UL-TTA), a fully training-free and backprop-free framework that freezes the backbone and adapts only logit-level parameters: class prototypes, class priors, and temperature. UL-TTA performs an online EM-style procedure with (i) selective sample filtering to use only confident predictions, (ii) closed-form Bayesian updates for prototypes and priors anchored by text and Dirichlet priors, (iii) decoupled temperatures for prediction vs. calibration, and (iv) lightweight guards (norm clipping, prior KL constraints, smoothed temperature) to prevent drift in long streams. Across large-scale cross-domain and OOD benchmarks (PACS, Office-Home, DomainNet, Terra Incognita, ImageNet-R/A/V2/Sketch; ~726K test samples) and strong TTA baselines including Tent, T3A, CoTTA, SAR, Tip-Adapter, and FreeTTA, UL-TTA consistently improves top-1 accuracy (e.g., +4.7 points over zero-shot CLIP on average) while reducing ECE by 20-30%, with less than 8% latency overhead. Long-stream experiments up to 200K samples show no collapse. Our results demonstrate that logit-level Bayesian adaptation is sufficient to obtain state-of-the-art accuracy-calibration trade-offs for VLMs under domain shift, without updating any backbone parameters.