Summary of Unveiling Backbone Effects in Clip: Exploring Representational Synergies and Variances, by Cristian Rodriguez-opazo and Edison Marrese-taylor and Ehsan Abbasnejad and Hamed Damirchi and Ignacio M. Jara and Felipe Bravo-marquez and Anton Van Den Hengel
Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances
by Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Ehsan Abbasnejad, Hamed Damirchi, Ignacio M. Jara, Felipe Bravo-Marquez, Anton van den Hengel
First submitted to arxiv on: 22 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 Contrastive Language-Image Pretraining (CLIP) is a popular method for learning image representations. This paper investigates how different neural architectures, including Vision Transformers (ViTs), Convolutional Networks (ConvNets) like ResNets, perform when trained with CLIP and used as universal backbones across various vision tasks. The study reveals significant differences in the performance of these architectures, even when using the same data and training objectives. Normalizing the learned representations also affects performance. Notably, combining predictions from multiple backbones can lead to a notable performance boost of up to 6.34%. The paper proposes a simple approach for achieving this boost and will release code for reproducing results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to learn about pictures by looking at words that describe them. This is called Contrastive Language-Image Pretraining (CLIP). Scientists have been testing different ways of doing this, using special computer programs like Vision Transformers (ViTs) and Convolutional Networks (ConvNets) like ResNets. They found that these programs work in different ways and can give different results even if they’re trained the same way. By combining their results, scientists might be able to get better answers. The paper suggests a simple way to do this and will share the code so others can try it out. |
Keywords
* Artificial intelligence * Pretraining
