Summary of Vlmine: Long-tail Data Mining with Vision Language Models, by Mao Ye et al.
VLMine: Long-Tail Data Mining with Vision Language Models
by Mao Ye, Gregory P. Meyer, Zaiwei Zhang, Dennis Park, Siva Karthik Mustikovela, Yuning Chai, Eric M Wolff
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A machine learning educator writing for a technical audience can summarize this abstract as follows: This paper addresses the problem of identifying rare examples within an unlabeled dataset, which is crucial in real-world applications like autonomous driving. The authors propose a simple and scalable data mining approach that utilizes a large vision language model (VLM) to summarize image content into keywords. By leveraging keyword frequency, they identify long-tail examples. Compared to conventional methods based on model uncertainty, the VLM offers a distinct signal for identifying rare examples. To integrate signals from multiple mining algorithms, the authors propose a simple and general approach. They evaluate their method on two tasks: 2D image classification and 3D object detection. The results show large improvements (10-50%) over baseline techniques on several benchmarks, including ImageNet-LT, Places-LT, and Waymo Open Dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For high school students or non-technical adults, this paper is about finding rare examples in a dataset without labeling them first. This is important for applications like self-driving cars. The authors use a big language model to summarize what’s in an image into keywords. They then find the rare examples by looking at how often these keywords appear. Their approach works better than usual methods and can be used with different types of data. They tested it on two tasks: identifying what’s in pictures and detecting objects in 3D. The results show that their method is much better than others, especially when dealing with rare examples. |
Keywords
» Artificial intelligence » Image classification » Language model » Machine learning » Object detection
