Summary of Aggregate-and-adapt Natural Language Prompts For Downstream Generalization Of Clip, by Chen Huang and Skyler Seto and Samira Abnar and David Grangier and Navdeep Jaitly and Josh Susskind
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP
by Chen Huang, Skyler Seto, Samira Abnar, David Grangier, Navdeep Jaitly, Josh Susskind
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 paper presents a novel approach called Aggregate-and-Adapted Prompt Embedding (AAPE) to improve prompt learning for large pretrained vision-language models. Specifically, it distills textual knowledge from natural language prompts to provide rich priors for under-represented visual concepts during finetuning. The authors develop a learned prompt aggregator to obtain a prompt summary aligned with each input image and optimize a joint loss function to produce a prompt embedding that stays close to the aggregated summary while minimizing task loss. Experimental results show that AAPE achieves competitive performance on various downstream tasks, including few-shot classification, VQA, and image captioning, and is particularly effective in handling non-canonical and OOD examples. The approach eliminates the need for LLM-based inference and scales better with data and model size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves prompt learning by using textual knowledge from natural language prompts to help large pretrained vision-language models learn new things. It’s like giving a hint or summary about what an image is, which helps the model understand it better. The authors develop a special way of combining these hints with the images and optimizing the process to make the model more accurate. They test this approach on many different tasks and show that it can do well even when there’s limited data available. It also does a good job handling unusual or out-of-the-ordinary examples. |
Keywords
» Artificial intelligence » Classification » Embedding » Few shot » Image captioning » Inference » Loss function » Prompt
