3 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
Segmentation Assisted Incremental Test Time Adaptation in an Open World
Manogna Sreenivas, Soma Biswas
In dynamic environments, unfamiliar objects and distribution shifts are often encountered, which challenge the generalization abilities of the deployed trained models. This work ad…
Efficient Open Set Single Image Test Time Adaptation of Vision Language Models
Manogna Sreenivas, Soma Biswas
Adapting models to dynamic, real-world environments characterized by shifting data distributions and unseen test scenarios is a critical challenge in deep learning. In this paper,…
pSTarC: Pseudo Source Guided Target Clustering for Fully Test-Time Adaptation
Manogna Sreenivas, Goirik Chakrabarty, Soma Biswas
Test Time Adaptation (TTA) is a pivotal concept in machine learning, enabling models to perform well in real-world scenarios, where test data distribution differs from training. In…
SATA: Source Anchoring and Target Alignment Network for Continual Test Time Adaptation
Goirik Chakrabarty, Manogna Sreenivas, Soma Biswas
Adapting a trained model to perform satisfactorily on continually changing testing domains/environments is an important and challenging task. In this work, we propose a novel frame…