Summary of Teach Clip to Develop a Number Sense For Ordinal Regression, by Yao Du et al.
Teach CLIP to Develop a Number Sense for Ordinal Regression
by Yao Du, Qiang Zhai, Weihang Dai, Xiaomeng Li
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 potential of pre-trained vision-language models (VLMs) in ordinal regression tasks, which involve predicting a specific order or ranking among objects. The authors investigate the performance of CLIP, a popular VLM, on ordinal regression tasks and find that it falls short due to its limitations in understanding compositional concepts like number sense. To overcome this limitation, they propose NumCLIP, a method that disassembles the task into coarse classification and fine prediction stages, leveraging the pre-trained alignment in CLIP. The authors also introduce a novel regularisation loss function that preserves both semantic and ordinal alignment in the feature space. Experimental results on three tasks demonstrate the effectiveness of NumCLIP, achieving 10% and 3.83% accuracy improvements on historical image dating and image aesthetics assessment tasks, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to predict the order or ranking of things, like how old a picture is or how good it looks. The researchers looked at how well a certain type of AI model (called CLIP) can do this job and found that it’s not very good because it doesn’t understand numbers well enough. They created a new method called NumCLIP to help the AI model be better at this task. They tested NumCLIP on three different tasks and found that it worked much better than the original model. |
Keywords
» Artificial intelligence » Alignment » Classification » Loss function » Regression
