Dhruv Batra

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Facebook ∙ Georgia Institute of Technology ∙ Virginia Polytechnic Institute and State University

Assistant Professor School of Interactive Computing (Georgia Tech) and Research Scientist (Facebook AI Research)

Habitat: A Platform for Embodied AI Research

We present Habitat, a new platform for research in embodied artificial i...
04/02/2019 ∙ by Manolis Savva, et al. ∙ 58 ∙ share

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Audio-Visual Scene-Aware Dialog

We introduce the task of scene-aware dialog. Given a follow-up question ...
01/25/2019 ∙ by Huda Alamri, et al. ∙ 48 ∙ share

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nocaps: novel object captioning at scale

Image captioning models have achieved impressive results on datasets con...
12/20/2018 ∙ by Harsh Agrawal, et al. ∙ 46 ∙ share

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Decentralized Distributed PPO: Solving PointGoal Navigation

We present Decentralized Distributed Proximal Policy Optimization (DD-PP...
11/01/2019 ∙ by Erik Wijmans, et al. ∙ 45 ∙ share

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Are We Making Real Progress in Simulated Environments? Measuring the Sim2Real Gap in Embodied Visual Navigation

Does progress in simulation translate to progress in robotics? Specifica...
12/13/2019 ∙ by Abhishek Kadian, et al. ∙ 37 ∙ share

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Embodied Multimodal Multitask Learning

Recent efforts on training visual navigation agents conditioned on langu...
02/04/2019 ∙ by Devendra Singh Chaplot, et al. ∙ 36 ∙ share

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Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded

Many vision and language models suffer from poor visual grounding - ofte...
02/11/2019 ∙ by Ramprasaath R. Selvaraju, et al. ∙ 26 ∙ share

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EvalAI: Towards Better Evaluation Systems for AI Agents

We introduce EvalAI, an open source platform for evaluating and comparin...
02/10/2019 ∙ by Deshraj Yadav, et al. ∙ 24 ∙ share

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Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering

We propose a new class of probabilistic neural-symbolic models, that hav...
02/21/2019 ∙ by Ramakrishna Vedantam, et al. ∙ 24 ∙ share

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Counterfactual Visual Explanations

A counterfactual query is typically of the form 'For situation X, why wa...
04/16/2019 ∙ by Yash Goyal, et al. ∙ 24 ∙ share

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TarMAC: Targeted Multi-Agent Communication

We explore a collaborative multi-agent reinforcement learning setting wh...
10/26/2018 ∙ by Abhishek Das, et al. ∙ 22 ∙ share

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Embodied Visual Recognition

Passive visual systems typically fail to recognize objects in the amodal...
04/09/2019 ∙ by Jianwei Yang, et al. ∙ 22 ∙ share

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Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition

In an open-world setting, it is inevitable that an intelligent agent (e....
10/01/2018 ∙ by Jianwei Yang, et al. ∙ 20 ∙ share

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CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog

Visual Dialog is a multimodal task of answering a sequence of questions ...
03/07/2019 ∙ by Satwik Kottur, et al. ∙ 18 ∙ share

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Improving Generative Visual Dialog by Answering Diverse Questions

Prior work on training generative Visual Dialog models with reinforcemen...
09/23/2019 ∙ by Vishvak Murahari, et al. ∙ 18 ∙ share

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Unsupervised Discovery of Decision States for Transfer in Reinforcement Learning

We present a hierarchical reinforcement learning (HRL) or options framew...
07/24/2019 ∙ by Nirbhay Modhe, et al. ∙ 17 ∙ share

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Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance

Individual neurons in convolutional neural networks supervised for image...
08/08/2018 ∙ by Ramprasaath R. Selvaraju, et al. ∙ 16 ∙ share

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

Prior work in visual dialog has focused on training deep neural models o...
12/05/2019 ∙ by Vishvak Murahari, et al. ∙ 16 ∙ share

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Talk the Walk: Navigating New York City through Grounded Dialogue

We introduce "Talk The Walk", the first large-scale dialogue dataset gro...
07/09/2018 ∙ by Harm de Vries, et al. ∙ 14 ∙ share

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Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future

In model-based reinforcement learning, the agent interleaves between mod...
03/05/2019 ∙ by Nan Rosemary Ke, et al. ∙ 14 ∙ share

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Pythia v0.1: the Winning Entry to the VQA Challenge 2018

This document describes Pythia v0.1, the winning entry from Facebook AI ...
07/26/2018 ∙ by Yu Jiang, et al. ∙ 12 ∙ share

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Response to "Visual Dialogue without Vision or Dialogue" (Massiceti et al., 2018)

In a recent workshop paper, Massiceti et al. presented a baseline model ...
01/16/2019 ∙ by Abhishek Das, et al. ∙ 12 ∙ share

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Embodied Question Answering in Photorealistic Environments with Point Cloud Perception

To help bridge the gap between internet vision-style problems and the go...
04/06/2019 ∙ by Erik Wijmans, et al. ∙ 12 ∙ share

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Towards VQA Models that can Read

Studies have shown that a dominant class of questions asked by visually ...
04/18/2019 ∙ by Amanpreet Singh, et al. ∙ 12 ∙ share

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Emergence of Compositional Language with Deep Generational Transmission

Consider a collaborative task that requires communication. Two agents ar...
04/19/2019 ∙ by Michael Cogswell, et al. ∙ 12 ∙ share

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Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning

Diverse and accurate vision+language modeling is an important goal to re...
08/22/2019 ∙ by Jyoti Aneja, et al. ∙ 12 ∙ share

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ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

We present ViLBERT (short for Vision-and-Language BERT), a model for lea...
08/06/2019 ∙ by Jiasen Lu, et al. ∙ 9 ∙ share

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Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations

Many structured prediction problems (particularly in vision and language...
06/08/2018 ∙ by Ashwin Kalyan, et al. ∙ 8 ∙ share

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Graph R-CNN for Scene Graph Generation

We propose a novel scene graph generation model called Graph R-CNN, that...
08/01/2018 ∙ by Jianwei Yang, et al. ∙ 6 ∙ share

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Neural Modular Control for Embodied Question Answering

We present a modular approach for learning policies for navigation over ...
10/26/2018 ∙ by Abhishek Das, et al. ∙ 6 ∙ share

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Multi-Target Embodied Question Answering

Embodied Question Answering (EQA) is a relatively new task where an agen...
04/09/2019 ∙ by Licheng Yu, et al. ∙ 6 ∙ share

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The Replica Dataset: A Digital Replica of Indoor Spaces

We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor s...
06/13/2019 ∙ by Julian Straub, et al. ∙ 4 ∙ share

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Chasing Ghosts: Instruction Following as Bayesian State Tracking

A visually-grounded navigation instruction can be interpreted as a seque...
07/03/2019 ∙ by Peter Anderson, et al. ∙ 3 ∙ share

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Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

Convolutional Neural Networks have achieved state-of-the-art performance...
11/19/2015 ∙ by Stefan Lee, et al. ∙ 0 ∙ share

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Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning

We develop the first approximate inference algorithm for 1-Best (and M-B...
05/24/2017 ∙ by Qing Sun, et al. ∙ 0 ∙ share

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LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation

We present LR-GAN: an adversarial image generation model which takes sce...
03/05/2017 ∙ by Jianwei Yang, et al. ∙ 0 ∙ share

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Grad-CAM: Why did you say that?

We propose a technique for making Convolutional Neural Network (CNN)-bas...
11/22/2016 ∙ by Ramprasaath R. Selvaraju, et al. ∙ 0 ∙ share

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Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

We propose a technique for producing "visual explanations" for decisions...
10/07/2016 ∙ by Ramprasaath R. Selvaraju, et al. ∙ 0 ∙ share

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Reducing Overfitting in Deep Networks by Decorrelating Representations

One major challenge in training Deep Neural Networks is preventing overf...
11/19/2015 ∙ by Michael Cogswell, et al. ∙ 0 ∙ share

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Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?

We conduct large-scale studies on `human attention' in Visual Question A...
06/17/2016 ∙ by Abhishek Das, et al. ∙ 0 ∙ share

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Joint Unsupervised Learning of Deep Representations and Image Clusters

In this paper, we propose a recurrent framework for Joint Unsupervised L...
04/13/2016 ∙ by Jianwei Yang, et al. ∙ 0 ∙ share

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Counting Everyday Objects in Everyday Scenes

We are interested in counting the number of instances of object classes ...
04/12/2016 ∙ by Prithvijit Chattopadhyay, et al. ∙ 0 ∙ share

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CloudCV: Large Scale Distributed Computer Vision as a Cloud Service

We are witnessing a proliferation of massive visual data. Unfortunately ...
06/12/2015 ∙ by Harsh Agrawal, et al. ∙ 0 ∙ share

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VIP: Finding Important People in Images

People preserve memories of events such as birthdays, weddings, or vacat...
02/19/2015 ∙ by Clint Solomon Mathialagan, et al. ∙ 0 ∙ share

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An Efficient Message-Passing Algorithm for the M-Best MAP Problem

Much effort has been directed at algorithms for obtaining the highest pr...
10/16/2012 ∙ by Dhruv Batra, et al. ∙ 0 ∙ share

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Combining the Best of Graphical Models and ConvNets for Semantic Segmentation

We present a two-module approach to semantic segmentation that incorpora...
12/14/2014 ∙ by Michael Cogswell, et al. ∙ 0 ∙ share

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Candidate Constrained CRFs for Loss-Aware Structured Prediction

When evaluating computer vision systems, we are often concerned with per...
12/10/2014 ∙ by Faruk Ahmed, et al. ∙ 0 ∙ share

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Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets

To cope with the high level of ambiguity faced in domains such as Comput...
11/06/2014 ∙ by Adarsh Prasad, et al. ∙ 0 ∙ share

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Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog

A number of recent works have proposed techniques for end-to-end learnin...
06/26/2017 ∙ by Satwik Kottur, et al. ∙ 0 ∙ share

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Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Much of human dialogue occurs in semi-cooperative settings, where agents...
06/16/2017 ∙ by Mike Lewis, et al. ∙ 0 ∙ share

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Dhruv Batra

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