U2A: Unified Unimodal Adaptation for Robust and Efficient Multimodal Learning

• URL: http://arxiv.org/abs/2501.17823v1
• Date: Wed, 29 Jan 2025 18:15:49 GMT
• Title: U2A: Unified Unimodal Adaptation for Robust and Efficient Multimodal Learning
• Authors: Md Kaykobad Reza, Niki Nezakati, Ameya Patil, Mashhour Solh, M. Salman Asif,
• Abstract summary: We present Unified Unimodal Adaptation (U2A), which jointly fine-tunes unimodal encoders using low-rank adaptation (LoRA) for various multimodal tasks.<n>Our method significantly reduces the number of learnable parameters and eliminates the need for complex training strategies.<n>Mask Tokens (MT) generate missing modality features from available modalities using a single token per modality.
• Score: 10.909746391230206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal learning often relies on designing new models and complex training strategies to achieve optimal performance. We present Unified Unimodal Adaptation (U2A), which jointly fine-tunes pretrained unimodal encoders using low-rank adaptation (LoRA) for various multimodal tasks. Our method significantly reduces the number of learnable parameters and eliminates the need for complex training strategies, such as alternating training, gradient modifications, or unimodal fine-tuning. To address missing modalities during both training and testing, we introduce Mask Tokens (MT), which generate missing modality features from available modalities using a single token per modality. This simplifies the process, removing the need for specialized feature estimation or prompt-tuning methods. Our evaluation demonstrates that U2A matches or outperforms state-of-the-art methods in both complete and missing modality settings, showcasing strong performance and robustness across various modalities, tasks, and datasets. We also analyze and report the effectiveness of Mask Tokens in different missing modality scenarios. Overall, our method provides a robust, flexible, and efficient solution for multimodal learning, with minimal computational overhead.

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