Summary of Context-aware Multimodal Pretraining, by Karsten Roth et al.
Context-Aware Multimodal Pretraining
by Karsten Roth, Zeynep Akata, Dima Damen, Ivana Balažević, Olivier J. Hénaff
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 A multimodal representation learning framework is proposed that enables zero-shot transfer and few-shot adaptation in vision-language models. The approach builds upon the standard pretraining paradigm by incorporating an objective function that encourages representations to accommodate additional context. This extension leads to significant improvements in sample efficiency and few-shot adaptation, with up to four-fold gains in test-time performance across 21 downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows how to make vision-language models better at adapting to new situations without needing a lot of training data. The model is trained on large amounts of image-text data, but then it’s given some extra information about the task it needs to do. This helps it learn to be more flexible and adapt quickly to new tasks, with gains of up to 5% in performance. |
Keywords
* Artificial intelligence * Few shot * Objective function * Pretraining * Representation learning * Zero shot
