9 citations · 18 across the 4 of their papers we have counts for
4 papers
Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision
Swetava Ganguli, C. V. Krishnakumar Iyer, Vipul Pandey
Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred…
Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs
Xuerong Xiao, Swetava Ganguli, Vipul Pandey
Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Converse…
Trinity: A No-Code AI platform for complex spatial datasets
C. V. Krishnakumar Iyer, Feili Hou, Henry Wang +4
We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domai…
VAE-Info-cGAN: Generating Synthetic Images by Combining Pixel-level and Feature-level Geospatial Conditional Inputs
Xuerong Xiao, Swetava Ganguli, Vipul Pandey
Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Converse…