Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances

• URL: http://arxiv.org/abs/2312.14400v1
• Date: Fri, 22 Dec 2023 03:01:41 GMT
• Title: Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances
• Authors: 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
• Abstract summary: Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. We investigate the differences in CLIP performance among various neural architectures. We propose a simple, yet effective approach to combine predictions from multiple backbones, leading to a notable performance boost of up to 6.34%.
• Score: 49.631908848868505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various neural architectures, spanning Transformer-based models like Vision Transformers (ViTs) to Convolutional Networks (ConvNets) like ResNets, are trained with CLIP and serve as universal backbones across diverse vision tasks. Despite utilizing the same data and training objectives, the effectiveness of representations learned by these architectures raises a critical question. Our investigation explores the differences in CLIP performance among these backbone architectures, revealing significant disparities in their classifications. Notably, normalizing these representations results in substantial performance variations. Our findings showcase a remarkable possible synergy between backbone predictions that could reach an improvement of over 20% through informed selection of the appropriate backbone. Moreover, we propose a simple, yet effective approach to combine predictions from multiple backbones, leading to a notable performance boost of up to 6.34\%. We will release the code for reproducing the results.

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