Summary of Analytic Continual Test-time Adaptation For Multi-modality Corruption, by Yufei Zhang et al.
Analytic Continual Test-Time Adaptation for Multi-Modality Corruption
by Yufei Zhang, Yicheng Xu, Hongxin Wei, Zhiping Lin, Huiping Zhuang
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach, Multi-modality Dynamic Analytic Adapter (MDAA), for Test-Time Adaptation (TTA) in multi-modal corruption scenarios. TTA aims to help pre-trained models adapt to unlabelled test data with domain shifts caused by corruption, such as weather changes or noise. The proposed MDAA approach innovatively introduces analytic learning into TTA using Analytic Classifiers (ACs) to prevent model forgetting. Additionally, it develops a Dynamic Selection Mechanism (DSM) and Soft Pseudo-label Strategy (SPS) to dynamically filter reliable samples and integrate information from different modalities. Experimental results demonstrate that MDAA achieves state-of-the-art performance on MM-CTTA tasks while ensuring reliable model adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn new things by looking at data they haven’t seen before. This is important because sometimes the data might be different from what the computer has learned already. For example, if a weather app needs to predict temperatures in rainy conditions, it should be able to adjust based on rain data. The paper proposes a new way to do this, called MDAA, which uses special techniques to help the computer learn and adapt quickly. It’s like having a super-smart filter that can separate good information from bad. By using this approach, the computer can make better predictions even when the data is different. |
Keywords
» Artificial intelligence » Multi modal
