CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks
Foundation models have revolutionized AI, but adapting them efficiently for multimodal tasks, particularly in dual-stream architectures composed of unimodal encoders, such as DINO and BERT, remains a significant challenge. ParameterEfficient Fine-Tuning (PEFT) methods like LowRank Adaptation (LoRA) enable lightweight adaptation, yet they operate in isolation within each modality, limiting their ability in capturing cross-modal interactions. In this paper, we take a step in bridging this gap with Cross-Modal LowRank Adaptation (CoLA), a novel PEFT framework that extends LoRA by introducing a dedicated inter-modal adaptation pathway alongside the standard intra-modal one. This dual-path des...
| Authors | Wish Suharitdamrong, Tony Alex, Muhammad Awais et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 13 August 2026 |
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