Summary of Countclip — [re] Teaching Clip to Count to Ten, by Harshvardhan Mestha et al.
CountCLIP – [Re] Teaching CLIP to Count to Ten
by Harshvardhan Mestha, Tejas Agrawal, Karan Bania, Shreyas V, Yash Bhisikar
First submitted to arxiv on: 5 Jun 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 paper proposes a method to improve the counting accuracy of large vision-language models (VLMs) while maintaining their performance in classification tasks. By introducing a counting-contrastive loss term and finetuning a CLIP model, the authors demonstrate improved zero-shot counting accuracy on a smaller training dataset with reduced computational resources. The approach is verified by reproducing the study using open-source code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that large vision-language models can learn to count objects in images, but they need help to do it accurately. The researchers improve the model’s counting skills by adding a special type of learning signal and reducing the amount of training data needed. This makes it possible for the model to learn to count objects quickly and efficiently. |
Keywords
* Artificial intelligence * Classification * Contrastive loss * Zero shot
