Summary of For: Finetuning For Object Level Open Vocabulary Image Retrieval, by Hila Levi et al.
FOR: Finetuning for Object Level Open Vocabulary Image Retrieval
by Hila Levi, Guy Heller, Dan Levi
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Retrieval (cs.IR); 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 Finetuning for Object-centric Open-vocabulary Image Retrieval (FOR) approach improves upon current methods by allowing finetuning on target datasets using closed-set labels. This technique builds upon a pre-trained CLIP model, incorporating a specialized decoder variant and multi-objective training framework to enhance accuracy in open vocabulary retrieval tasks. In comparison to state-of-the-art (SoTA), FOR achieves significant improvements of up to 8 mAP@50 points across three evaluated datasets. Additionally, the approach demonstrates effectiveness in semi-supervised settings, even when only a small portion of the dataset is labeled. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find pictures that match what we’re looking for by using words to describe them. Right now, people usually use a special kind of computer program called CLIP to do this job. The problem with CLIP is that it doesn’t always get things right. To fix this, the researchers came up with a new way to train CLIP so it can find better matches. This new method is called FOR and it uses two important parts: a special decoder that helps understand what words mean, and a special way of training that makes sure the computer program gets really good at finding the right pictures. With this new approach, they were able to make the computer program much more accurate, which is very useful for things like searching through lots of images to find specific ones. |
Keywords
» Artificial intelligence » Decoder » Semi supervised
