Summary of Clip with Quality Captions: a Strong Pretraining For Vision Tasks, by Pavan Kumar Anasosalu Vasu et al.
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks
by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Oncel Tuzel
First submitted to arxiv on: 14 May 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 paper explores the limitations of Contrastive Language-Image Pre-training (CLIP) models on dense prediction tasks like object detection and semantic segmentation. While CLIP excels at zero-shot classification and retrieval, recent studies have shown that its learnt representations are not well-suited for downstream tasks. To address this, the authors introduce a simple yet effective approach: improving the quality of captions in image-text datasets improves the quality of CLIP’s visual representations. This results in significant improvements on dense prediction vision tasks, surpassing state-of-the-art masked image modeling (MIM) pretraining methods like Masked Autoencoder (MAE). The authors demonstrate this with ViT-B/16 as the image encoder, achieving 12.1% higher mean intersection over union (mIoU) and 11.5% lower root mean squared error (RMSE) on semantic segmentation and depth estimation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how a special type of AI model called CLIP can be improved to perform better at certain tasks like recognizing objects in images or understanding the meaning of sentences. Right now, CLIP is really good at understanding what’s in an image without being specifically trained for that task, but it doesn’t do as well when asked to do more specific things like detect objects or understand the context of a sentence. The researchers found that by making sure the text and images used to train the model are closely related, they can make CLIP much better at these tasks. This is important because it could help us use AI models in all sorts of situations where we want them to be able to understand what’s going on. |
Keywords
» Artificial intelligence » Autoencoder » Classification » Depth estimation » Encoder » Mae » Object detection » Pretraining » Semantic segmentation » Vit » Zero shot
