Summary of Make Prompts Adaptable: Bayesian Modeling For Vision-language Prompt Learning with Data-dependent Prior, by Youngjae Cho et al.
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
by Youngjae Cho, HeeSun Bae, Seungjae Shin, Yeo Dong Youn, Weonyoung Joo, Il-Chul Moon
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 Recent advancements in Vision-Language Pretrained (VLP) models have revolutionized various downstream tasks. However, these models are typically utilized as frozen representations without learning. To overcome this limitation, prompt learning has emerged as a promising approach by incorporating a learnable context vector into the text encoder’s inputs. In few-shot learning scenarios, maximum likelihood estimation (MLE) training can lead to overfitting of dominant image features in the training data, potentially hindering generalization abilities. This paper proposes a Bayesian-based framework for prompt learning, addressing overfitting issues and enhancing adaptability on unseen instances. By modeling data-dependent priors, our approach balances performance between seen and unseen image features without sacrificing accuracy. We demonstrate the efficacy of our method on benchmark datasets, showcasing statistically significant improvements compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper focuses on making machines better at understanding images with text. Right now, these machines are trained as one big block that can’t change or learn new things. To fix this, scientists have developed a way to add a special “prompt” to the machine’s language processing part. This prompt helps the machine understand the image and text together. Sometimes, when training the machine on a small number of examples, it can get too good at recognizing certain features in those examples and forget how to recognize other important features. To solve this problem, the scientists developed a new approach that combines some clever math with the prompt idea. They tested their approach on several image recognition tasks and found that it performed much better than existing methods. |
Keywords
* Artificial intelligence * Encoder * Few shot * Generalization * Likelihood * Overfitting * Prompt
