Research papers, conference publications, and patented innovations in machine learning, computer vision, and graph neural networks.
Prathmesh Bele, Valay Bundele, Avigyan Bhattacharya, Ankit Jha, Gemma Roig, Biplab Banerjee
In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV'24)
Developed SODG-Net, an end-to-end network outperforming benchmarks by 1-14% in open-domain settings. Proposed a style synthesis block generating diverse statistical features to effectively simulate novel domains. Formulated novel weight learning and margin objectives to establish distinct representations for open classes.
Prathmesh Bele*, Apurva Kulkarni*, Hitul Desai, Amey Kavde, Dashrath Mali, Laxmikant Sahoo, et al.
Intellectual Property of India, Reference No. 202341087789
Proposed a novel GNN architecture formulating free-space mapping as a scalable edge-prediction problem. Architected a dual-path Graph Attention Network to independently process node and edge features. Implemented class-aware edge retention via feature fusion, achieving parity with inflexible rule-based legacy systems.