Summary of Aple: Token-wise Adaptive For Multi-modal Prompt Learning, by Guiming Cao et al.
APLe: Token-Wise Adaptive for Multi-Modal Prompt Learning
by Guiming Cao, Kaize Shi, Hong Fu, Huaiwen Zhang, Guandong Xu
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This research paper proposes a novel approach to improve the generalization performance of pre-trained Vision-Language (V-L) models, which have set the benchmark for downstream tasks. The authors explore the challenges of sensitivity to text input and tuning processes across multi-modal prompts. Building upon recent advances in learnable prompts, they introduce Token-wise Adaptive for Multi-modal Prompt Learning (APLe), a sequential training process that adapts different modalities branches of CLIP efficiently. APLe addresses the challenges in V-L models by promoting prompt learning across both modalities, achieving competitive generalization performance and robustness in various experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart AI model that can understand images and words. This model is really good at doing tasks like image classification or object detection. But, it’s not perfect and sometimes needs help to do certain tasks well. The researchers in this paper found a way to improve the model’s performance by giving it special instructions called “prompts”. These prompts are like secret codes that help the model understand what it should be looking for in an image or text. They developed a new method called APLe, which helps the model learn these prompts more effectively and make better decisions. This means the model can do tasks even better than before! |
Keywords
» Artificial intelligence » Generalization » Image classification » Multi modal » Object detection » Prompt » Token
