activity
20152025
most citedMake-A-Video: Text-to-Video Generation without Text-Video Data

315 citations · 955 across the 39 of their papers we have counts for

collaborators
Showing 2019 · cs.LGShow all

7 papers · 2 filters

cs.LG2019

Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline

Vishvak Murahari, Dhruv Batra, Devi Parikh +1

Prior work in visual dialog has focused on training deep neural models on VisDial in isolation. Instead, we present an approach to leverage pretraining on related vision-language d…

cs.LG2019

Improving Generative Visual Dialog by Answering Diverse Questions

Vishvak Murahari, Prithvijit Chattopadhyay, Dhruv Batra +2

Prior work on training generative Visual Dialog models with reinforcement learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown that this 'self-talk' approach…

cs.LG2019

IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL

Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma +4

We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic cont…

cs.LG2019

Emergence of Compositional Language with Deep Generational Transmission

Michael Cogswell, Jiasen Lu, Stefan Lee +2

Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task. Of particular interest are the factors that cause…

cs.LG201938 cited

Counterfactual Visual Explanations

Yash Goyal, Ziyan Wu, Jan Ernst +3

In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image for which a vision system predicts class , a counterfactual visual…

cs.LG2019

Embodied Multimodal Multitask Learning

Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov +2

Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for different multimodal tas…