MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings

TL;DR

MoCa introduces a two-stage modality-aware pre-training framework that enhances bidirectional multimodal embeddings by addressing limitations of causal attention, scalability, and data diversity, leading to state-of-the-art results.

Contribution

It proposes a novel two-stage framework with joint reconstruction and diverse data fine-tuning to improve multimodal embeddings over existing causal VLMs.

Findings

Achieves new state-of-the-art on MMEB and ViDoRe-v2 benchmarks.

Demonstrates strong scalability with model size and data volume.

Improves bidirectional reasoning in multimodal embeddings.

Abstract

Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use of causal attention in VLM backbones is suboptimal for embedding tasks; scalability issues due to reliance on high-quality labeled paired data for contrastive learning; and limited diversity in training objectives and data. To address these issues, we propose MoCa, a two-stage framework for transforming pre-trained VLMs into effective bidirectional multimodal embedding models. The first stage, Modality-aware Continual Pre-training, introduces a joint reconstruction objective that simultaneously denoises interleaved text and image inputs, enhancing bidirectional context-aware reasoning. The second stage, Heterogeneous Contrastive Fine-tuning, leverages diverse, semantically rich multimodal data beyond simple image-caption pairs to enhance generalization and alignment. Our method addresses the stated limitations by introducing bidirectional attention through continual pre-training, scaling effectively with massive unlabeled datasets via joint reconstruction objectives, and utilizing diverse multimodal data for enhanced representation robustness. Experiments demonstrate that MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results, and exhibits strong scalability with both model size and training data on MMEB.