42 citations · 111 across the 9 of their papers we have counts for
9 papers
Which way is `right'?: Uncovering limitations of Vision-and-Language Navigation model
Meera Hahn, Amit Raj, James M. Rehg
The challenging task of Vision-and-Language Navigation (VLN) requires embodied agents to follow natural language instructions to reach a goal location or object (e.g. `walk down th…
Transformer-based Localization from Embodied Dialog with Large-scale Pre-training
Meera Hahn, James M. Rehg
We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who i…
Learning a Visually Grounded Memory Assistant
Meera Hahn, Kevin Carlberg, Ruta Desai +1
We introduce a novel interface for large scale collection of human memory and assistance. Using the 3D Matterport simulator we create a realistic indoor environments in which we ha…
No RL, No Simulation: Learning to Navigate without Navigating
Meera Hahn, Devendra Chaplot, Shubham Tulsiani +3
Most prior methods for learning navigation policies require access to simulation environments, as they need online policy interaction and rely on ground-truth maps for rewards. How…
Where Are You? Localization from Embodied Dialog
Meera Hahn, Jacob Krantz, Dhruv Batra +4
We present Where Are You? (WAY), a dataset of ~6k dialogs in which two humans -- an Observer and a Locator -- complete a cooperative localization task. The Observer is spawned at r…
Tripping through time: Efficient Localization of Activities in Videos
Meera Hahn, Asim Kadav, James M. Rehg +1
Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video. Previous works have ap…
Action2Vec: A Crossmodal Embedding Approach to Action Learning
Meera Hahn, Andrew Silva, James M. Rehg
We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video cli…
Learning to Localize and Align Fine-Grained Actions to Sparse Instructions
Meera Hahn, Nataniel Ruiz, Jean-Baptiste Alayrac +2
Automatic generation of textual video descriptions that are time-aligned with video content is a long-standing goal in computer vision. The task is challenging due to the difficult…
Deep Tracking: Visual Tracking Using Deep Convolutional Networks
Meera Hahn, Si Chen, Afshin Dehghan
In this paper, we study a discriminatively trained deep convolutional network for the task of visual tracking. Our tracker utilizes both motion and appearance features that are ext…