Summary of Language Plays a Pivotal Role in the Object-attribute Compositional Generalization Of Clip, by Reza Abbasi et al.
Language Plays a Pivotal Role in the Object-Attribute Compositional Generalization of CLIP
by Reza Abbasi, Mohammad Samiei, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
First submitted to arxiv on: 27 Mar 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 This paper investigates the out-of-distribution (OoD) generalization capabilities of vision-language models, specifically CLIP. Recent studies have shown that these models can generalize well under various distribution shifts, but the reasons behind this capability are not yet fully understood. The authors focus on a specific type of OoD data – images with novel compositions of attribute-object pairs – and design an authentic image test dataset called ImageNet-AO to study whether CLIPs can successfully classify these images into composition classes. They find that CLIPs trained with large datasets such as OpenAI CLIP, LAION-400M, and LAION-2B show orders-of-magnitude improvement in compositional OoD generalization compared to both supervised models and CLIPs trained with smaller datasets. The results provide evidence that the scale and diversity of training data and language supervision play a key role in unlocking the compositional generalization abilities of vision-language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computer vision models can recognize new images they’ve never seen before. These models, like CLIP, are really good at recognizing things even when they’re shown unusual pictures. But what makes them so good? The researchers wanted to find out if it’s because the models were trained on lots of different types of pictures or if there’s something else going on. They made a special set of test images with weird combinations of objects and attributes, like a car with wheels that are also ears. Then they tested how well CLIP could recognize these new images. What they found is that when the models were trained on lots and lots of different pictures, they did much better at recognizing the new images than if they were trained on fewer pictures. |
Keywords
* Artificial intelligence * Generalization * Supervised
