Abstract Video Question Answering (VideoQA) is the task of answering questions about a
video. At its core is understanding the alignments between visual scenes in video and …

State-of-the-art models often make use of superficial patterns in the data that do not
generalize well to out-of-domain or adversarial settings. For example, textual entailment …

Abstract Visual Question Answering (VQA) is the task of answering questions about an
image. Some VQA models often exploit unimodal biases to provide the correct answer …

We present a large-scale study of imitating human demonstrations on tasks that require a
virtual robot to search for objects in new environments-(1) ObjectGoal Navigation (eg'find & …

To help bridge the gap between internet vision-style problems and the goal of vision for
embodied perception we instantiate a large-scale navigation task--Embodied Question …

Language grounded image understanding tasks have often been proposed as a method for
evaluating progress in artificial intelligence. Ideally, these tasks should test a plethora of …

Neural generative models have been become increasingly popular when building
conversational agents. They offer flexibility, can be easily adapted to new domains, and …

We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve
the generalization ability for semantic visual navigation agents in unseen environments …

Vision-language navigation (VLN) tasks require an agent to follow language instructions
from a human guide to navigate in previously unseen environments using visual …

The promise of multimodal models for real-world applications has inspired research in
visualizing and understanding their internal mechanics with the end goal of empowering …