2 citations · 5 across the 7 of their papers we have counts for
7 papers
Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs
Sanjay Bhargav Dharavath, Tanmoy Dam, Supriyo Chakraborty +2
The field of autonomous vehicles (AVs) predominantly leverages multi-modal integration of LiDAR and camera data to achieve better performance compared to using a single modality. H…
OrthoSeisnet: Seismic Inversion through Orthogonal Multi-scale Frequency Domain U-Net for Geophysical Exploration
Supriyo Chakraborty, Aurobinda Routray, Sanjay Bhargav Dharavath +1
Seismic inversion is crucial in hydrocarbon exploration, particularly for detecting hydrocarbons in thin layers. However, the detection of sparse thin layers within seismic dataset…
Unlocking the capabilities of explainable fewshot learning in remote sensing
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large am…
Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting
Md Rasel Sarkar, Sreenatha G. Anavatti, Tanmoy Dam +2
The main objective of this study is to propose an enhanced wind power forecasting (EWPF) transformer model for handling power grid operations and boosting power market competition.…
Scalable Adversarial Online Continual Learning
Tanmoy Dam, Mahardhika Pratama, MD Meftahul Ferdaus +2
Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susce…
Latent Preserving Generative Adversarial Network for Imbalance classification
Tanmoy Dam, Md Meftahul Ferdaus, Mahardhika Pratama +3
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend…