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Contrastive Language-Image Pretraining (CLIP) stands out as a prominentmethod for image representation learning. Various neural architectures,spanning Transformer-based models like Vision Transformers (ViTs) toConvolutional Networks (ConvNets) like ResNets, are trained with CLIP and serveas universal backbones across diverse vision tasks. Despite utilizing the samedata and training objectives, the effectiveness of representations learned bythese architectures raises a critical question. Our investigation explores thedifferences in CLIP performance among these backbone architectures, revealingsignificant disparities in their classifications. Notably, normalizing theserepresentations results in substantial performance variations. Our findingsshowcase a remarkable possible synergy between backbone predictions that couldreach an improvement of over 20% through informed selection of the appropriatebackbone. Moreover, we propose a simple, yet effective approach to combinepredictions from multiple backbones, leading to a notable performance boost ofup to 6.34\%. We will release the code for reproducing the results.

Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances