37 citations · 39 across the 3 of their papers we have counts for
9 papers
Group Equivariant Deep Reinforcement Learning
Arnab Kumar Mondal, Pratheeksha Nair, Kaleem Siddiqi
In Reinforcement Learning (RL), Convolutional Neural Networks(CNNs) have been successfully applied as function approximators in Deep Q-Learning algorithms, which seek to learn acti…
Appearance Shock Grammar for Fast Medial Axis Extraction from Real Images
Charles-Olivier Dufresne Camaro, Morteza Rezanejad, Stavros Tsogkas +2
We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervi…
Affinity Graph Supervision for Visual Recognition
Chu Wang, Babak Samari, Vladimir G. Kim +2
Affinity graphs are widely used in deep architectures, including graph convolutional neural networks and attention networks. Thus far, the literature has focused on abstracting fea…
Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition
Chu Wang, Marcello Pelillo, Kaleem Siddiqi
View based strategies for 3D object recognition have proven to be very successful. The state-of-the-art methods now achieve over 90% correct category level recognition performance…
FAN: Focused Attention Networks
Chu Wang, Babak Samari, Vladimir Kim +2
Attention networks show promise for both vision and language tasks, by emphasizing relationships between constituent elements through weighting functions. Such elements could be re…
DeepFlux for Skeletons in the Wild
Yukang Wang, Yongchao Xu, Stavros Tsogkas +3
Computing object skeletons in natural images is challenging, owing to large variations in object appearance and scale, and the complexity of handling background clutter. Many recen…