13 citations · 13 across the 1 of their papers we have counts for
6 papers
What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
Kiana Ehsani, Daniel Gordon, Thomas Nguyen +2
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approache…
Watching the World Go By: Representation Learning from Unlabeled Videos
Daniel Gordon, Kiana Ehsani, Dieter Fox +1
Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks. The basic principle in these works is instance discrimination: le…
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon +5
We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to seque…
SplitNet: Sim2Sim and Task2Task Transfer for Embodied Visual Navigation
Daniel Gordon, Abhishek Kadian, Devi Parikh +2
We propose SplitNet, a method for decoupling visual perception and policy learning. By incorporating auxiliary tasks and selective learning of portions of the model, we explicitly…
What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning
Daniel Gordon, Dieter Fox, Ali Farhadi
Long-term planning poses a major difficulty to many reinforcement learning algorithms. This problem becomes even more pronounced in dynamic visual environments. In this work we pro…
Shifting the Baseline: Single Modality Performance on Visual Navigation & QA
Jesse Thomason, Daniel Gordon, Yonatan Bisk
We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research. Where existing work oft…