Search Results for author:
Found 7 papers, 5 papers with code
EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata
We learn a visual representation that captures information about the camera that recorded a given photo.
TEACh: Task-driven Embodied Agents that Chat
Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes.
VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator
Interactive robots navigating photo-realistic environments need to be trained to effectively leverage and handle the dynamic nature of dialogue in addition to the challenges underlying vision-and-language navigation (VLN).
Sim-to-Real Transfer for Vision-and-Language Navigation
We study the challenging problem of releasing a robot in a previously unseen environment, and having it follow unconstrained natural language navigation instructions.
Extended Abstract: Improving Vision-and-Language Navigation with Image-Text Pairs from the Web
Following a navigation instruction such as 'Walk down the stairs and stop near the sofa' requires an agent to ground scene elements referenced via language (e. g.'stairs') to visual content in the environment (pixels corresponding to 'stairs').
Improving Vision-and-Language Navigation with Image-Text Pairs from the Web
Following a navigation instruction such as 'Walk down the stairs and stop at the brown sofa' requires embodied AI agents to ground scene elements referenced via language (e. g. 'stairs') to visual content in the environment (pixels corresponding to 'stairs').
Ranked #6 on
Vision and Language Navigation
on VLN Challenge
Chasing Ghosts: Instruction Following as Bayesian State Tracking
Our experiments show that our approach outperforms a strong LingUNet baseline when predicting the goal location on the map.
