Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Dam, Tanmoy"'
Autor:
Dharavath, Sanjay Bhargav, Dam, Tanmoy, Chakraborty, Supriyo, Roy, Prithwiraj, Maiti, Aniruddha
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. However, the fusion process encounters challenges in detecting distant
Externí odkaz:
http://arxiv.org/abs/2408.11207
Autor:
Lee, Gao Yu, Chen, Jinkuan, Dam, Tanmoy, Ferdaus, Md Meftahul, Poenar, Daniel Puiu, Duong, Vu N
High-quality images are crucial in remote sensing and UAV applications, but atmospheric haze can severely degrade image quality, making image dehazing a critical research area. Since the introduction of deep convolutional neural networks, numerous ap
Externí odkaz:
http://arxiv.org/abs/2405.07520
Autor:
Dam, Tanmoy, Dharavath, Sanjay Bhargav, Alam, Sameer, Lilith, Nimrod, Chakraborty, Supriyo, Feroskhan, Mir
Publikováno v:
2024 IEEE International Conference on Robotics and Automation (ICRA)
Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems. Yet, the fusion encounters difficulties with extended distance detection due to the contrast between LiDAR's sparse data a
Externí odkaz:
http://arxiv.org/abs/2402.07680
Seismic inversion is crucial in hydrocarbon exploration, particularly for detecting hydrocarbons in thin layers. However, the detection of sparse thin layers within seismic datasets presents a significant challenge due to the ill-posed nature and poo
Externí odkaz:
http://arxiv.org/abs/2401.04393
Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural netwo
Externí odkaz:
http://arxiv.org/abs/2310.08619
Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend
Externí odkaz:
http://arxiv.org/abs/2304.10811
Autor:
Sarkar, Md Rasel, Anavatti, Sreenatha G., Dam, Tanmoy, Pratama, Mahardhika, Kindhi, Berlian Al
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. It helps reliable large-scale integration of wind power relies in lar
Externí odkaz:
http://arxiv.org/abs/2304.10758
In real world scenarios, out-of-distribution (OOD) datasets may have a large distributional shift from training datasets. This phenomena generally occurs when a trained classifier is deployed on varying dynamic environments, which causes a significan
Externí odkaz:
http://arxiv.org/abs/2209.02881
Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susceptibility to the catastrophic forgetting problem. Nevertheless, the AC
Externí odkaz:
http://arxiv.org/abs/2209.01558
Autor:
Dam, Tanmoy, Ferdaus, Md Meftahul, Pratama, Mahardhika, Anavatti, Sreenatha G., Jayavelu, Senthilnath, Abbass, Hussein A.
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 to be biased towards the majority class, leaving the classifier vulne
Externí odkaz:
http://arxiv.org/abs/2209.01555