# BayesTTA: Continual-Temporal Test-Time Adaptation for Vision-Language Models via Gaussian Discriminant Analysis

arXiv: https://arxiv.org/abs/2507.08607 · HTML: https://mlsanity.com/paper/2507.08607/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 5 of its 85 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 5 of 5)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation](https://mlsanity.com/paper/2406.10737/) (2024)
- [DOTA: Distributional Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2409.19375/) (2024)

## Built on by (most cited first, 5 of 5)

- [Adapting Vision-Language Models Without Labels: A Comprehensive Survey](https://mlsanity.com/paper/2508.05547/) (2025)
- [ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2606.28719/) (2026)
- [Multi-modal Test-time Adaptation via Adaptive Probabilistic Gaussian Calibration](https://mlsanity.com/paper/2604.19093/) (2026)
- [Towards Continual Test-Time Adaptation of Vision-Language Models in Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2608.29923/) (2026)
- [Not Every Correction Helps: Gain-Guided Continual Test-Time Adaptation](https://mlsanity.com/paper/2609.36655/) (2026)

## Abstract (arXiv)

Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but degrade significantly under textittemporally evolving distribution shifts common in real-world scenarios (e.g., gradual illumination or seasonal changes). Existing continual test-time adaptation (CTTA) methods are typically built around sudden and severe distribution shifts and neglect temporal continuity, leading to three core defects: limited memory cache restricts long-range distribution modeling, causing catastrophic forgetting; entropy-based confidence becomes unreliable under temporal drift, worsening error accumulation; and static visual representations misalign with evolving inputs. We formalize this practical problem as textitContinual-Temporal Test-Time Adaptation (CT-TTA), where test distributions evolve gradually over time. To address it, we propose textitBayesTTA, a Bayesian adaptation framework that enforces temporally consistent predictions and dynamically aligns visual representations. Specifically, BayesTTA incrementally estimates class-conditional Gaussian mixture distributions without storing raw data, adaptively selects covariance structures through statistical hypothesis testing, and performs calibrated inference using Gaussian discriminant analysis (GDA). These calibrated predictions supervise self-paced adaptation of normalization layers, ensuring efficient and stable representation alignment. We establish a comprehensive CT-TTA benchmark across four temporally evolving datasets and further evaluate generalization on ten standard TTA datasets. Extensive experiments show that BayesTTA consistently outperforms state-of-the-art methods, achieving significant gains while maintaining efficiency. Code is available at hrefhttps://github.com/cuishuang99/BayesTTAhttps://github.com/cuishuang99/BayesTTA.