Summary of Learning to Rank Pre-trained Vision-language Models For Downstream Tasks, by Yuhe Ding et al.
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks
by Yuhe Ding, Bo Jiang, Aihua Zheng, Qin Xu, Jian Liang
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed Visual-tExtual Graph Alignment (VEGA) method enables the selection of vision language models (VLMs) without requiring annotations or large-scale supervised datasets. By leveraging the alignment between visual and textual features, VEGA measures the similarity between the two modalities on downstream tasks. This approach is motivated by the pretraining paradigm of VLMs, which maps both modalities into a shared representation space. The method constructs graphs on vision and textual features and defines VEGA as the overall similarity between these graphs at node and edge levels. Experimental results across three benchmarks demonstrate the reliability and accuracy of VEGA in estimating VLM performance on unlabeled downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find the best way for a computer to understand pictures without needing lots of labeled training data. This is called “unsupervised” learning, and it’s really important because we can’t always get huge amounts of labeled data. A new method called VEGA helps computers choose the right way to do this by looking at how well different models align pictures with words. This works because when we train these models, we’re actually teaching them to connect similar things between pictures and words. So, VEGA uses special graphs to compare these connections between pictures and words and picks the best model for a job. In tests, VEGA did really well at choosing the right model for different tasks! |
Keywords
» Artificial intelligence » Alignment » Pretraining » Supervised » Unsupervised
