BREEN: Bridge Data-Efficient Encoder-Free Multimodal Learning with Learnable Queries

• URL: http://arxiv.org/abs/2503.12446v1
• Date: Sun, 16 Mar 2025 10:43:14 GMT
• Title: BREEN: Bridge Data-Efficient Encoder-Free Multimodal Learning with Learnable Queries
• Authors: Tianle Li, Yongming Rao, Winston Hu, Yu Cheng,
• Abstract summary: multimodal large language models (MLLMs) eliminate the need for a well-trained vision encoder by directly processing image tokens before the language model.<n>The absence of a vision encoder implies that the model is likely to rely on substantial data to learn the necessary visual-semantic alignments.<n>We present BREEN, a data-efficient encoder-free multimodal architecture that mitigates this issue.
• Score: 37.37905881898424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Encoder-free multimodal large language models(MLLMs) eliminate the need for a well-trained vision encoder by directly processing image tokens before the language model. While this approach reduces computational overhead and model complexity, it often requires large amounts of training data to effectively capture the visual knowledge typically encoded by vision models like CLIP. The absence of a vision encoder implies that the model is likely to rely on substantial data to learn the necessary visual-semantic alignments. In this work, we present BREEN, a data-efficient encoder-free multimodal architecture that mitigates this issue. BREEN leverages a learnable query and image experts to achieve comparable performance with significantly less training data. The learnable query, positioned between image and text tokens, is supervised by the output of a pretrained CLIP model to distill visual knowledge, bridging the gap between visual and textual modalities. Additionally, the image expert processes image tokens and learnable queries independently, improving efficiency and reducing interference with the LLM's textual capabilities. BREEN achieves comparable performance to prior encoder-free state-of-the-art models like Mono-InternVL, using only 13 million text-image pairs in training about one percent of the data required by existing methods. Our work highlights a promising direction for data-efficient encoder-free multimodal learning, offering an alternative to traditional encoder-based approaches.

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