Summary of Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-attention For Fine-grained Few-shot Learning, by Eric Brouwer et al.
Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning
by Eric Brouwer, Jan Erik van Woerden, Gertjan Burghouts, Matias Valdenegro-Toro, Marco Zullich
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 method enhances the Contrastive Language-Image Pre-Training (CLIP) model through adaptive prompt tuning, guided by real-time visual inputs. Unlike existing techniques like Context Optimization (CoOp) and Visual Prompt Tuning (VPT), which rely on static prompts or visual tokens, this approach leverages a cross-attention mechanism to dynamically refine text prompts for the image at hand. This enables an image-specific alignment of textual features with image patches extracted from the Vision Transformer, making the model more effective for datasets with high intra-class variance and low inter-class differences. The method is evaluated on several datasets, including CUBirds, Oxford Flowers, and FGVC Aircraft, showing significant performance gains over static prompt tuning approaches. To ensure these performance gains translate into trustworthy predictions, Monte-Carlo Dropout is integrated to improve the reliability of model predictions and uncertainty estimates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to teach computers to recognize small differences in images with just a few examples. It uses a special kind of language-image matching called CLIP and makes it better by adjusting the text prompts based on the image. This helps when there are many similar classes and not much data. The method works well on several different datasets, showing improvement over other methods that use static text or visual cues. To make sure the predictions are reliable, the paper also shows how to measure uncertainty and confidence. |
Keywords
» Artificial intelligence » Alignment » Cross attention » Dropout » Optimization » Prompt » Vision transformer
