FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens
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.
684002718bd5bff9918ce018
Christian Schlarmann
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2025-06-04T08:53:34.919Z
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Christian Schlarmann
user
chs20
684002718bd5bff9918ce019
Francesco Croce
684002718bd5bff9918ce01a
Nicolas Flammarion
684002718bd5bff9918ce01b
Matthias Hein
FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens
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.
FuseLIP is a transformer-based architecture that uses a shared vocabulary for text and image tokens to enhance multimodal embedding and outperforms existing models in tasks such as VQA and text-guided image retrieval.
contrastive language-image pre-training latent space unimodal encoders multimodal inputs feature vector late fusion early fusion transformer model discrete image tokenizers multimodal pre-training VQA text-guided image transformation retrieval