12 citations · 14 across the 11 of their papers we have counts for
11 papers
AetherVision-Bench: An Open-Vocabulary RGB-Infrared Benchmark for Multi-Angle Segmentation across Aerial and Ground Perspectives
Aniruddh Sikdar, Aditya Gandhamal, Suresh Sundaram
Open-vocabulary semantic segmentation (OVSS) involves assigning labels to each pixel in an image based on textual descriptions, leveraging world models like CLIP. However, they enc…
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…
IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks
Manjunath D, Prajwal Gurunath, Sumanth Udupa +4
Deep neural networks (DNNs) have shown exceptional performance when trained on well-illuminated images captured by Electro-Optical (EO) cameras, which provide rich texture details.…
Syn2Real Domain Generalization for Underwater Mine-like Object Detection Using Side-Scan Sonar
Aayush Agrawal, Aniruddh Sikdar, Rajini Makam +3
Underwater mine detection with deep learning suffers from limitations due to the scarcity of real-world data. This scarcity leads to overfitting, where models perform well on train…
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…
Contrastive Learning-Based Spectral Knowledge Distillation for Multi-Modality and Missing Modality Scenarios in Semantic Segmentation
Aniruddh Sikdar, Jayant Teotia, Suresh Sundaram
Improving the performance of semantic segmentation models using multispectral information is crucial, especially for environments with low-light and adverse conditions. Multi-modal…
MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
Sumanth Udupa, Prajwal Gurunath, Aniruddh Sikdar +1
Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains, but due to the absence of style diversity during training, enhancing…
Fully complex-valued deep learning model for visual perception
Aniruddh Sikdar, Sumanth Udupa, Suresh Sundaram
Deep learning models operating in the complex domain are used due to their rich representation capacity. However, most of these models are either restricted to the first quadrant o…
Fully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC2MFN) for Building Segmentation of InSAR images
Aniruddh Sikdar, Sumanth Udupa, Suresh Sundaram +1
Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance. Although complex-valued deep learning networks perform…
Multi-Modal Domain Fusion for Multi-modal Aerial View Object Classification
Sumanth Udupa, Aniruddh Sikdar, Suresh Sundaram
Object detection and classification using aerial images is a challenging task as the information regarding targets are not abundant. Synthetic Aperture Radar(SAR) images can be use…