LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning

• URL: http://arxiv.org/abs/2503.04812v1
• Date: Tue, 04 Mar 2025 10:21:57 GMT
• Title: LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning
• Authors: Zhibin Lan, Liqiang Niu, Fandong Meng, Jie Zhou, Jinsong Su,
• Abstract summary: We propose a framework that dynamically improves the embedding model's representation learning for negative pairs.<n>LLaVE establishes stronger baselines that achieve state-of-the-art (SOTA) performance.<n>LLaVE can generalize to text-video retrieval tasks in a zero-shot manner and achieve strong performance.
• Score: 76.82159851648711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Universal multimodal embedding models play a critical role in tasks such as interleaved image-text retrieval, multimodal RAG, and multimodal clustering. However, our empirical results indicate that existing LMM-based embedding models trained with the standard InfoNCE loss exhibit a high degree of overlap in similarity distribution between positive and negative pairs, making it challenging to distinguish hard negative pairs effectively. To deal with this issue, we propose a simple yet effective framework that dynamically improves the embedding model's representation learning for negative pairs based on their discriminative difficulty. Within this framework, we train a series of models, named LLaVE, and evaluate them on the MMEB benchmark, which covers 4 meta-tasks and 36 datasets. Experimental results show that LLaVE establishes stronger baselines that achieve state-of-the-art (SOTA) performance while demonstrating strong scalability and efficiency. Specifically, LLaVE-2B surpasses the previous SOTA 7B models, while LLaVE-7B achieves a further performance improvement of 6.2 points. Although LLaVE is trained on image-text data, it can generalize to text-video retrieval tasks in a zero-shot manner and achieve strong performance, demonstrating its remarkable potential for transfer to other embedding tasks.

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