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An Empirical Study of Multimodal Model Merging

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arxiv 2304.14933 v2 pith:YS63IM62 submitted 2023-04-28 cs.CV cs.AIcs.CLcs.LG

An Empirical Study of Multimodal Model Merging

Yi-Lin Sung , Linjie Li , Kevin Lin , Zhe Gan , Mohit Bansal , Lijuan Wang This is my paper
classification cs.CV cs.AIcs.CLcs.LG
keywords mergingmodeltaskstrainedarchitecturedifferentinitializationmodality-agnostic
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Model merging (e.g., via interpolation or task arithmetic) fuses multiple models trained on different tasks to generate a multi-task solution. The technique has been proven successful in previous studies, where the models are trained on similar tasks and with the same initialization. In this paper, we expand on this concept to a multimodal setup by merging transformers trained on different modalities. Furthermore, we conduct our study for a novel goal where we can merge vision, language, and cross-modal transformers of a modality-specific architecture to create a parameter-efficient modality-agnostic architecture. Through comprehensive experiments, we systematically investigate the key factors impacting model performance after merging, including initialization, merging mechanisms, and model architectures. We also propose two metrics that assess the distance between weights to be merged and can serve as an indicator of the merging outcomes. Our analysis leads to an effective training recipe for matching the performance of the modality-agnostic baseline (i.e., pre-trained from scratch) via model merging. Our method also outperforms naive merging significantly on various tasks, with improvements of 3% on VQA, 7% on COCO retrieval, 25% on NLVR2, 14% on Flickr30k and 3% on ADE20k. Our code is available at https://github.com/ylsung/vl-merging

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Cited by 3 Pith papers

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  1. AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization

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    AdaMMS merges heterogeneous MLLMs via architecture mapping, linear weight interpolation, and unsupervised hyper-parameter search, outperforming prior methods on vision-language benchmarks as the first such approach wi...

  2. PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging

    cs.CV 2026-04 unverdicted novelty 6.0

    PivotMerge merges heterogeneous multimodal pre-trained models via shared-space decomposition to filter conflicts and layer-wise weights based on alignment contributions, outperforming baselines on multimodal benchmarks.

  3. PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging

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    PivotMerge merges post-alignment cross-modal projectors from heterogeneous MLLM pre-training via shared-space filtering and layer-wise weights, beating prior merging baselines on multimodal benchmarks.