Vision Transformer Adapters for Generalizable Multitask Learning

• URL: http://arxiv.org/abs/2308.12372v1
• Date: Wed, 23 Aug 2023 18:40:48 GMT
• Title: Vision Transformer Adapters for Generalizable Multitask Learning
• Authors: Deblina Bhattacharjee, Sabine S\"usstrunk, Mathieu Salzmann
• Abstract summary: We introduce the first multitasking vision transformer adapters that learn generalizable task affinities. Our adapters can simultaneously solve multiple dense vision tasks in a parameter-efficient manner. In contrast to concurrent methods, we do not require retraining or fine-tuning whenever a new task or domain is added.
• Score: 61.79647180647685
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains. Integrated into an off-the-shelf vision transformer backbone, our adapters can simultaneously solve multiple dense vision tasks in a parameter-efficient manner, unlike existing multitasking transformers that are parametrically expensive. In contrast to concurrent methods, we do not require retraining or fine-tuning whenever a new task or domain is added. We introduce a task-adapted attention mechanism within our adapter framework that combines gradient-based task similarities with attention-based ones. The learned task affinities generalize to the following settings: zero-shot task transfer, unsupervised domain adaptation, and generalization without fine-tuning to novel domains. We demonstrate that our approach outperforms not only the existing convolutional neural network-based multitasking methods but also the vision transformer-based ones. Our project page is at \url{https://ivrl.github.io/VTAGML}.

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