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)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 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
