Summary of Extract Free Dense Misalignment From Clip, by Jeongyeon Nam et al.
Extract Free Dense Misalignment from CLIP
by JeongYeon Nam, Jinbae Im, Wonjae Kim, Taeho Kil
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The proposed CLIP4DM method detects dense misalignments between images and text, outperforming zero-shot models while being more efficient than fine-tuned models. Building on the pre-trained CLIP model, CLIP4DM identifies misaligned words by revising the gradient-based attribution computation to include negative gradients for individual tokens. This approach is then combined with a global alignment score in F-CLIPScore. Evaluations on various benchmarks show state-of-the-art performance among zero-shot models and competitive performance with fine-tuned models, while maintaining superior efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of detecting mistakes between what we see (images) and what we say (text) is proposed. The method, called CLIP4DM, uses a pre-trained model to find words that don’t match the image. It does this by looking at how much each word affects the result, even if it’s in a negative way. This helps identify when an object or attribute isn’t shown in the image but is mentioned in the text. The method performs well on different datasets and types of misalignments. |
Keywords
» Artificial intelligence » Alignment » Zero shot
