no code implementations • 5 Jan 2024 • Sudipan Saha, Tushar Verma, Dario Augusto Borges Oliveira
With the global population on the rise, our cities have been expanding to accommodate the growing number of people.
no code implementations • 11 Sep 2023 • Shan Zhao, Sudipan Saha, Zhitong Xiong, Niklas Boers, Xiao Xiang Zhu
Motivated by this, we explore a geometric deep learning-based temporal Graph Convolutional Network (GCN) for precipitation nowcasting.
1 code implementation • 5 May 2023 • Iris de Gélis, Sudipan Saha, Muhammad Shahzad, Thomas Corpetti, Sébastien Lefèvre, Xiao Xiang Zhu
To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning.
1 code implementation • CVPR2022W 2022 • Codruţ-Andrei Diaconu, Sudipan Saha, Stephan Günnemann, Xiao Xiang Zhu
Climate change is perhaps the biggest single threat to humankind and the environment, as it severely impacts our terrestrial surface, home to most of the living species.
Ranked #2 on Earth Surface Forecasting on EarthNet2021 OOD Track
no code implementations • 5 Oct 2021 • Lukas Kondmann, Aysim Toker, Sudipan Saha, Bernhard Schölkopf, Laura Leal-Taixé, Xiao Xiang Zhu
It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for change detection.
no code implementations • 26 Aug 2021 • Sudipan Saha, Shan Zhao, Nasrullah Sheikh, Xiao Xiang Zhu
Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains.
no code implementations • 9 Jul 2021 • Sudipan Saha, Lichao Mou, Muhammad Shahzad, Xiao Xiang Zhu
The proposed method exploits this property to sample smaller patches from the larger scene and uses deep clustering and contrastive learning to refine the weights of a lightweight deep model composed of a series of the convolution layers along with an embedded channel attention.
no code implementations • 26 Apr 2021 • Konstantin Klemmer, Sudipan Saha, Matthias Kahl, Tianlin Xu, Xiao Xiang Zhu
Deep generative models are increasingly used to gain insights in the geospatial data domain, e. g., for climate data.
no code implementations • 9 Apr 2021 • Sudipan Saha, Biplab Banerjee, Xiao Xiang Zhu
Deep learning (DL) based supervised change detection (CD) models require large labeled training data.
no code implementations • 9 Apr 2021 • Jakob Gawlikowski, Sudipan Saha, Anna Kruspe, Xiao Xiang Zhu
In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences in the geographic area.
1 code implementation • 15 Mar 2021 • Lichao Mou, Sudipan Saha, Yuansheng Hua, Francesca Bovolo, Lorenzo Bruzzone, Xiao Xiang Zhu
To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning.
no code implementations • 12 Feb 2021 • Sudipan Saha, Patrick Ebel, Xiao Xiang Zhu
In particular, we are interested in the combination of the images acquired by optical and Synthetic Aperture Radar (SAR) sensors.
no code implementations • 9 Feb 2021 • Jay Nandy, Sudipan Saha, Wynne Hsu, Mong Li Lee, Xiao Xiang Zhu
In this paper, we propose a novel method, called \emph{Certification through Adaptation}, that transforms an AT model into a randomized smoothing classifier during inference to provide certified robustness for $\ell_2$ norm without affecting their empirical robustness against adversarial attacks.
1 code implementation • 31 Jan 2021 • Sudipan Saha, Nasrullah Sheikh
The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis.
no code implementations • 26 Sep 2020 • Sudipan Saha, Tahir Ahmad
This is an hindrance to the further development of AI.