no code implementations • 29 Nov 2023 • Angeela Acharya, Sulabh Shrestha, Anyi Chen, Joseph Conte, Sanja Avramovic, Siddhartha Sikdar, Antonios Anastasopoulos, Sanmay Das
Previous research has addressed this data limitation by incorporating medical ontologies and employing transfer learning methods.
no code implementations • 17 Nov 2023 • Yimeng Li, Navid Rajabi, Sulabh Shrestha, Md Alimoor Reza, Jana Kosecka
We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments, with the ultimate goal of facilitating the training of lightweight models for various downstream tasks.
no code implementations • 4 Oct 2022 • Sulabh Shrestha, Yimeng Li, Jana Kosecka
Given the spatial and temporal consistency cues used for pixel level data association, we use a variant of contrastive learning to train a DCNN model for predicting semantic segmentation from RGB views in the target environment.