8 citations · 8 across the 5 of their papers we have counts for
5 papers
Vision Transformers Learn Gestalt-Like Figure-Ground Cues from Natural Images
Matthias Tangemann, Benjamin Lo, Zygmunt Pizlo +3
Figure-ground organization in the human visual system relies on several shape-based cues, including surroundedness, convexity, and symmetry. While these cues have been extensively…
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory
Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar +2
The accurate modeling of dynamics in interactive environments is critical for successful long-range prediction. Such a capability could advance Reinforcement Learning (RL) and Plan…
MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
Mohammad Khodadad, Morteza Rezanejad, Ali Shiraee Kasmaee +3
The analysis of 3D point clouds has diverse applications in robotics, vision and graphics. Processing them presents specific challenges since they are naturally sparse, can vary in…
Mini-batch graphs for robust image classification
Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi
Current deep learning models for classification tasks in computer vision are trained using mini-batches. In the present article, we take advantage of the relationships between samp…
Ice Core Science Meets Computer Vision: Challenges and Perspectives
P. Bohleber, M. Roman, C. Barbante +3
Polar ice cores play a central role in studies of the earth's climate system through natural archives. A pressing issue is the analysis of the oldest, highly thinned ice core secti…