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cs.CV2025
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…
cs.CV2024
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,…
cs.CV2023
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…