FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens
Authors: name: Christian Schlarmann, name: Francesco Croce, name: Nicolas Flammarion, name: Matthias Hein

Contrastive language-image pre-training aligns the features of text-image
pairs in a common latent space via distinct encoders for each modality. While
this approach achieves impressive performance in several zero-shot tasks, it
cannot natively handle multimodal inputs, i.e., encoding image and text into a
single feature vector. As a remedy, it is common practice to use additional
modules to merge the features extracted by the unimodal encoders. In this work,
we present FuseLIP, an alternative architecture for multimodal embedding.
Leveraging recent progress in discrete image tokenizers, we propose to use a
single transformer model which operates on an extended vocabulary of text and
image tokens. This early fusion approach allows the different modalities to
interact at each depth of encoding and obtain richer representations compared
to common late fusion. We collect new datasets for multimodal pre-training and
evaluation, designing challenging tasks for multimodal encoder models. We show
that FuseLIP outperforms other approaches in multimodal embedding tasks such as
VQA and text-guided image transformation retrieval, while being comparable to
baselines on unimodal tasks.