jiaojuncao/MoVE-KD-7b-v1.1

【CVPR 2025】MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders
Jiajun Cao, Yuan Zhang, Tao Huang, Ming Lu, Qizhe Zhang, Ruichuan An, Ningning MA, Shanghang Zhang

Overview

Visual encoders are fundamental components in vision-language models (VLMs), each showcasing unique strengths derived from various pre-trained visual foundation models. To leverage the various capabilities of these encoders, recent studies incorporate multiple encoders within a single VLM, leading to a considerable increase in computational cost. In this paper, we present Mixture-of-Visual-Encoder Knowledge Distillation (MoVE-KD), a novel framework that distills the unique proficiencies of multiple vision encoders into a single, efficient encoder model. Specifically, to mitigate conflicts and retain the unique characteristics of each teacher encoder, we employ low-rank adaptation (LoRA) and mixture-of-experts (MoEs) to selectively activate specialized knowledge based on input features, enhancing both adaptability and efficiency. To regularize the KD process and enhance performance, we propose an attention-based distillation strategy that adaptively weighs the different visual encoders and emphasizes valuable visual tokens, reducing the burden of replicating comprehensive but distinct features from multiple teachers.

Code: https://github.com/hey-cjj/MoVE-KD

MoVE-KD Weights

Method	LLM	VQAv2	GQA	TextVQA	VizWiz	POPE	SQA	MME	MMB
LLaVA-v1.5	Vicuna-7B	78.5	62.0	58.2	50.0	85.9	66.8	1510.7	64.3
MoVE-KD-v1.0	Vicuna-7B	79.5	63.2	58.3	52.3	86.9	69.3	1524.5	66.3
MoVE-KD-v1.1	Vicuna-7B	79.9	63.9	59.6	52.7	86.3	69.8	1509.1	67.4
LLaVA-v1.5	Vicuna-13B	80.0	63.3	61.3	53.6	85.9	71.6	1531.3	67.7
MoVE-KD-v1.0	Vicuna-13B	80.6	64.2	59.7	55.7	85.7	73.2	1568.1	70.2
MoVE-KD-v1.1	Vicuna-13B	80.8	63.9	61.1	57.5	86.3	71.8	1568.3	69.7