Summary of Dual Prototype Evolving For Test-time Generalization Of Vision-language Models, by Ce Zhang et al.
Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models
by Ce Zhang, Simon Stepputtis, Katia Sycara, Yaqi Xie
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel test-time adaptation approach for vision-language models (VLMs) is introduced, addressing the limitation of current methods that focus on adapting VLMs from a single modality. The proposed Dual Prototype Evolving (DPE) method accumulates task-specific knowledge from multi-modalities by creating and evolving two sets of prototypes – textual and visual – to capture accurate multi-modal representations for target classes during test time. Learnable residuals are also introduced to promote consistent multi-modal representations. Experimental results on 15 benchmark datasets show that DPE outperforms previous state-of-the-art methods while maintaining competitive computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Test-time adaptation is a technique that helps models generalize to new, unseen data. Recently, researchers have applied this to special types of AI models called vision-language models (VLMs). These VLMs can understand images and text. But current methods only work well for single types of data. To fix this, scientists created a new method called Dual Prototype Evolving (DPE). DPE helps VLMs learn from multiple types of data by creating two sets of prototypes – one for text and one for images. This allows the model to understand relationships between different types of data better. The results show that DPE works well and is efficient. |
Keywords
» Artificial intelligence » Multi modal
