no code implementations • 11 Apr 2024 • Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Guennemann
It is an inference-based method that does not require training on the domain dataset and relies on extracting relevant features from self-supervised pre-trained models.
no code implementations • 8 Apr 2024 • Sai Bhargav Rongali, Sarthak Mehrotra, Ankit Jha, Mohamad Hassan N C, Shirsha Bose, Tanisha Gupta, Mainak Singha, Biplab Banerjee
In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes.
no code implementations • 31 Mar 2024 • Mainak Singha, Ankit Jha, Shirsha Bose, Ashwin Nair, Moloud Abdar, Biplab Banerjee
Central to our approach is modeling a unique prompt tailored for detecting unknown class samples, and to train this, we employ a readily accessible stable diffusion model, elegantly generating proxy images for the open class.
1 code implementation • 12 Apr 2023 • Mainak Singha, Ankit Jha, Bhupendra Solanki, Shirsha Bose, Biplab Banerjee
APPLeNet emphasizes the importance of multi-scale feature learning in RS scene classification and disentangles visual style and content primitives for domain generalization tasks.
no code implementations • 18 Feb 2023 • Shirsha Bose, Ritesh Sur Chowdhury, Debabrata Pal, Shivashish Bose, Biplab Banerjee, Subhasis Chaudhuri
Efficient road and building footprint extraction from satellite images are predominant in many remote sensing applications.
no code implementations • 18 Feb 2023 • Shirsha Bose, Ankit Jha, Enrico Fini, Mainak Singha, Elisa Ricci, Biplab Banerjee
Our method focuses on a domain-agnostic prompt learning strategy, aiming to disentangle the visual style and content information embedded in CLIP's pre-trained vision encoder, enabling effortless adaptation to novel domains during inference.