Summary of Catlip: Clip-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-text Data, by Sachin Mehta and Maxwell Horton and Fartash Faghri and Mohammad Hossein Sekhavat and Mahyar Najibi and Mehrdad Farajtabar and Oncel Tuzel and Mohammad Rastegari
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data
by Sachin Mehta, Maxwell Horton, Fartash Faghri, Mohammad Hossein Sekhavat, Mahyar Najibi, Mehrdad Farajtabar, Oncel Tuzel, Mohammad Rastegari
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method reframes pre-training on image-text data as a classification task, eliminating the need for pairwise similarity computations in contrastive loss and achieving a remarkable 2.7acceleration in training speed compared to contrastive learning on web-scale data. The paper presents extensive experiments spanning diverse vision tasks, including detection and segmentation, demonstrating that the proposed method maintains high representation quality. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how we can train AI models faster using large amounts of image-text data. Instead of comparing images and text to find similarities, they turn this task into a classification problem. This makes it much faster and still produces good results. They tested their approach on different tasks like object detection and segmentation, and it worked well. |
Keywords
» Artificial intelligence » Classification » Contrastive loss » Object detection