Summary of Synergy and Diversity in Clip: Enhancing Performance Through Adaptive Backbone Ensembling, by Cristian Rodriguez-opazo and Ehsan Abbasnejad and Damien Teney and Hamed Damirchi and Edison Marrese-taylor and Anton Van Den Hengel
Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling
by Cristian Rodriguez-Opazo, Ehsan Abbasnejad, Damien Teney, Hamed Damirchi, Edison Marrese-Taylor, Anton van den Hengel
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 explores the differences between various vision backbones trained with Contrastive Language-Image Pretraining (CLIP). The authors find that these architectures have distinct representations, classification performance, and robustness properties despite using the same data and training objective. This leads to a potential synergy across backbones by leveraging their strengths. The authors develop an approach to adaptively ensemble multiple backbones, which can achieve a remarkable increase in accuracy of up to 39.1% over the best single backbone on a large collection of datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how different ways of training computer vision models with words and pictures (called CLIP) result in different models that are good at different tasks. Even though they all use the same data, some models are better at recognizing certain things or handling noisy images. The authors show that by combining these models together, we can get even better results than using just one model alone. This could be useful for tasks like recognizing objects or classifying pictures. |
Keywords
» Artificial intelligence » Classification » Pretraining
