Reconstructing Content via Collaborative Attention to Improve Multimodal Embedding Quality

TL;DR

This paper introduces CoCoA, a novel content reconstruction pre-training paradigm that enhances multimodal embedding quality by restructuring attention and encouraging semantic compression, leading to more compact and informative representations.

Contribution

The paper proposes CoCoA, a new pre-training method based on collaborative attention and EOS reconstruction, improving the semantic quality of multimodal embeddings.

Findings

CoCoA significantly improves embedding quality on MMEB-V1.

Content reconstruction enhances the semantic compression of multimodal models.

The approach raises the performance ceiling of existing multimodal embedding models.

Abstract

Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classification. However, most existing approaches rely heavily on large-scale contrastive learning, with limited exploration of how the architectural and training paradigms of MLLMs affect embedding quality. While effective for generation, the causal attention and next-token prediction paradigm of MLLMs does not explicitly encourage the formation of globally compact representations, limiting their effectiveness as multimodal embedding backbones. To address this, we propose CoCoA, a Content reconstruction pre-training paradigm based on Collaborative Attention for multimodal embedding optimization. Specifically, we restructure the attention flow and introduce an EOS-based reconstruction task, encouraging the model to reconstruct input from the corresponding <EOS> embeddings. This drives the multimodal model to compress the semantic information of the input into the <EOS> token, laying the foundations for subsequent contrastive learning. Extensive experiments on MMEB-V1 demonstrate that CoCoA built upon Qwen2-VL and Qwen2.5-VL significantly improves embedding quality. Results validate that content reconstruction serves as an effective strategy to maximize the value of existing data, enabling multimodal embedding models generate compact and informative representations, raising their performance ceiling.