Test-Time Visual In-Context Tuning
This addresses the problem of domain adaptation in computer vision for researchers and practitioners, offering an incremental improvement over existing VICL methods.
The paper tackles the poor generalizability of visual in-context learning (VICL) under distribution shifts by proposing test-time Visual In-Context Tuning (VICT), which adapts VICL models on the fly with a single test sample, improving performance across six vision tasks with 15 corruptions.
Visual in-context learning (VICL), as a new paradigm in computer vision, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. While effective, the existing VICL paradigm exhibits poor generalizability under distribution shifts. In this work, we propose test-time Visual In-Context Tuning (VICT), a method that can adapt VICL models on the fly with a single test sample. Specifically, we flip the role between the task prompts and the test sample and use a cycle consistency loss to reconstruct the original task prompt output. Our key insight is that a model should be aware of a new test distribution if it can successfully recover the original task prompts. Extensive experiments on six representative vision tasks ranging from high-level visual understanding to low-level image processing, with 15 common corruptions, demonstrate that our VICT can improve the generalizability of VICL to unseen new domains. In addition, we show the potential of applying VICT for unseen tasks at test time. Code: https://github.com/Jiahao000/VICT.