126 citations · 158 across the 5 of their papers we have counts for
6 papers
Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
William Shen, Ge Yang, Alan Yu +3
Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed under…
Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments
Fei Xia, William B. Shen, Chengshu Li +6
We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interactio…
Situational Fusion of Visual Representation for Visual Navigation
Bokui Shen, Danfei Xu, Yuke Zhu +3
A complex visual navigation task puts an agent in different situations which call for a diverse range of visual perception abilities. For example, to "go to the nearest chair", the…
Taskonomy: Disentangling Task Transfer Learning
Amir Zamir, Alexander Sax, William Shen +3
Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions…
Visual Forecasting by Imitating Dynamics in Natural Sequences
Kuo-Hao Zeng, William B. Shen, De-An Huang +2
We introduce a general framework for visual forecasting, which directly imitates visual sequences without additional supervision. As a result, our model can be applied at several s…
Feedback Networks
Amir R. Zamir, Te-Lin Wu, Lin Sun +3
Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through…