Segmentation Assisted Incremental Test Time Adaptation in an Open World
It addresses the problem of continuous adaptation to emerging data for deployed models in open-world settings, representing an incremental improvement over traditional test time adaptation methods.
This work tackles incremental test time adaptation for vision language models in dynamic environments with unseen classes and domains, proposing a segmentation-assisted active labeling module (SegAssist) that enhances model performance in real-world scenarios, as demonstrated through extensive experiments on benchmark datasets.
In dynamic environments, unfamiliar objects and distribution shifts are often encountered, which challenge the generalization abilities of the deployed trained models. This work addresses Incremental Test Time Adaptation of Vision Language Models, tackling scenarios where unseen classes and unseen domains continuously appear during testing. Unlike traditional Test Time Adaptation approaches, where the test stream comes only from a predefined set of classes, our framework allows models to adapt simultaneously to both covariate and label shifts, actively incorporating new classes as they emerge. Towards this goal, we establish a new benchmark for ITTA, integrating single image TTA methods for VLMs with active labeling techniques that query an oracle for samples potentially representing unseen classes during test time. We propose a segmentation assisted active labeling module, termed SegAssist, which is training free and repurposes the segmentation capabilities of VLMs to refine active sample selection, prioritizing samples likely to belong to unseen classes. Extensive experiments on several benchmark datasets demonstrate the potential of SegAssist to enhance the performance of VLMs in real world scenarios, where continuous adaptation to emerging data is essential. Project-page:https://manogna-s.github.io/segassist/