no code implementations • NeurIPS 2023 • Zichen Zhang, Johannes Kirschner, Junxi Zhang, Francesco Zanini, Alex Ayoub, Masood Dehghan, Dale Schuurmans
A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle.
29 code implementations • 18 May 2020 • Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R. Zaiane, Martin Jagersand
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Ranked #1 on
Salient Object Detection
on SOD
no code implementations • 5 Mar 2020 • Jun Jin, Laura Petrich, Masood Dehghan, Martin Jagersand
We consider the problem of visual imitation learning without human supervision (e. g. kinesthetic teaching or teleoperation), nor access to an interactive reinforcement learning (RL) training environment.
1 code implementation • 2 Mar 2020 • Chen Jiang, Masood Dehghan, Martin Jagersand
In this paper, to model the intended concepts of manipulation, we present a vision dataset under a strictly constrained knowledge domain for both robot and human manipulations, where manipulation concepts and relations are stored by an ontology system in a taxonomic manner.
1 code implementation • 29 Sep 2018 • Jun Jin, Laura Petrich, Masood Dehghan, Zichen Zhang, Martin Jagersand
Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification.
Robotics
2 code implementations • 30 Apr 2017 • Xuebin Qin, Shida He, Camilo Perez Quintero, Abhineet Singh, Masood Dehghan, Martin Jagersand
The tracking scheme is coherently integrated into a perceptual grouping framework in which the visual tracking problem is tackled by identifying a subset of these line segments and connecting them sequentially to form a closed boundary with the largest saliency and a certain similarity to the previous one.