From Unimodal to Multimodal:Scaling up Projectors to Align Modalities

Mayug Maniparambil, Raiymbek Akshulakov, YASSER ABDELAZIZ DAHOU DJILALI, Sanath Narayan, Ankit Singh, Noel O'Connor

Keywords: vision language models, vision language alignment, unimodal models, representational alignment

TL;DR: This paper presents a method to align vision and language models using projection layers on frozen unimodal encoders, achieving competitive zero-shot performance with significantly reduced data and compute requirements.

Abstract: Recent contrastive multimodal vision-language models like CLIP have demonstrated robust open-world semantic understanding, becoming the standard image backbones for vision-language applications due to their aligned latent space. However, this practice has left powerful unimodal encoders for both vision and language underutilized in these applications which raises a key question: Is there a plausible way to connect unimodal backbones for zero-shot vision-language tasks? To this end, we propose a novel approach that aligns vision and language modalities using only projection layers on pretrained, frozen unimodal encoders. Our method exploits the high semantic similarity between embedding spaces of well-trained vision and language models. It involves selecting semantically similar encoders in the latent space, curating a concept-rich dataset of image-caption pairs, and training simple MLP projectors. We evaluated our approach on 12 zero-shot classification datasets and 2 image-text retrieval datasets. Our best model, utilizing DINOv2 and All-Roberta-Large text encoder, achieves 76\(\%\) accuracy on ImageNet with a 20-fold reduction in data and 65-fold reduction in compute requirements. The proposed framework enhances the accessibility of model development while enabling flexible adaptation across diverse scenarios, offering an efficient approach to building multimodal models by utilizing existing unimodal architectures. Code and datasets will be released upon acceptance.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 4011