2 citations · 3 across the 10 of their papers we have counts for
10 papers
OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation
Aditya Gandhamal, Aniruddh Sikdar, Suresh Sundaram
Open-vocabulary semantic segmentation (OVSS) entails assigning semantic labels to each pixel in an image using textual descriptions, typically leveraging world models such as CLIP.…
SAGA: Semantic-Aware Gray color Augmentation for Visible-to-Thermal Domain Adaptation across Multi-View Drone and Ground-Based Vision Systems
Manjunath D, Aniruddh Sikdar, Prajwal Gurunath +2
Domain-adaptive thermal object detection plays a key role in facilitating visible (RGB)-to-thermal (IR) adaptation by reducing the need for co-registered image pairs and minimizing…
Supervised Image Translation from Visible to Infrared Domain for Object Detection
Prahlad Anand, Qiranul Saadiyean, Aniruddh Sikdar +2
This study aims to learn a translation from visible to infrared imagery, bridging the domain gap between the two modalities so as to improve accuracy on downstream tasks including…
A Resource-Efficient Decentralized Sequential Planner for Spatiotemporal Wildfire Mitigation
Josy John, Shridhar Velhal, Suresh Sundaram
This paper proposes a Conflict-aware Resource-Efficient Decentralized Sequential planner (CREDS) for early wildfire mitigation using multiple heterogeneous Unmanned Aerial Vehicles…
Variable-Pitch-Propeller Mechanism Design, and Development of Heliquad for Mid-flight Flipping and Fault-Tolerant-Control
Eeshan Kulkarni, Suresh Sundaram
This paper presents the design of Variable-Pitch-Propeller mechanism and its application on a quadcopter called Heliquad to demonstrate its unique capabilities. The input-output re…
Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)
Sourav Mishra, Shirin Dora, Suresh Sundaram
Algorithms designed for addressing typical supervised classification problems can only learn from a fixed set of samples and labels, making them unsuitable for the real world, wher…