Recent advances in deep learning techniques have made impressive progress in many areas of computer vision, including classification, detection, and segmentation. While all of these areas are relevant to robotics applications, robotics also presents many unique challenges which require new approaches. Challenges include the need for real-time analysis, the need for accurate 3d understanding of scenes, and the difficulty of doing experiments at scale. There are also opportunities which robotics brings to computer vision, for example, the ability to use depth sensors, to control where the camera is looking, and to provide a data source for “grounded” learning of concepts, reducing the need for manual labeling. We will consider work related to deep learning techniques in computer vision applied to a broad range of robotic devices, from self driving cars to drones to bipedal robots.
The workshop is organised in conjunction with CVPR 2017 (the Computer Vision and Pattern Recognition conference) in Honolulu, Hawaii on July 21.
Jitendra Malik (UC Berkeley)
Raquel Urtasun (U Toronto)
Dieter Fox (U Washington)
Honglak Lee (Google Brain / U Michigan)
Abhinav Gupta (CMU)
Jianxiong Xiao (AutoX)
Andrew Davison (Imperial College London)
Richard Newcombe (Facebook)
Raia Hadsell (Google DeepMind)
Ashutosh Saxena (Brain of Things)
The workshop is supported by Google and the Australian Centre for Robotic Vision.
For more information, please check the workshop website at http://Juxi.net/workshop/deep-learning-robotic-vision-cvpr-2017/
Australian Centre for Robotic Vision
2 George Street Brisbane, 4001
+61 7 3138 7549