OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation

• URL: http://arxiv.org/abs/2412.09585v1
• Date: Thu, 12 Dec 2024 18:55:18 GMT
• Title: OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation
• Authors: Jitesh Jain, Zhengyuan Yang, Humphrey Shi, Jianfeng Gao, Jianwei Yang,
• Abstract summary: The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision. We propose OLA-VLM, the first approach distilling knowledge into the LLM's hidden representations from a set of target visual representations. We show that OLA-VLM boosts performance by an average margin of up to 2.5% on various benchmarks, with a notable improvement of 8.7% on the Depth task in CV-Bench.
• Score: 95.78870389271832
• License:
• Abstract: The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision. In this work, we posit an overlooked opportunity to optimize the intermediate LLM representations through a vision perspective (objective), i.e., solely natural language supervision is sub-optimal for the MLLM's visual understanding ability. To that end, we propose OLA-VLM, the first approach distilling knowledge into the LLM's hidden representations from a set of target visual representations. Firstly, we formulate the objective during the pretraining stage in MLLMs as a coupled optimization of predictive visual embedding and next text-token prediction. Secondly, we investigate MLLMs trained solely with natural language supervision and identify a positive correlation between the quality of visual representations within these models and their downstream performance. Moreover, upon probing our OLA-VLM, we observe improved representation quality owing to the embedding optimization. Thirdly, we demonstrate that our OLA-VLM outperforms the single and multi-encoder baselines, proving our approach's superiority over explicitly feeding the corresponding features to the LLM. Particularly, OLA-VLM boosts performance by an average margin of up to 2.5% on various benchmarks, with a notable improvement of 8.7% on the Depth task in CV-Bench. Our code is open-sourced at https://github.com/SHI-Labs/OLA-VLM .

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