Devi Parikh

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

Associate Professor School of Interactive Computing at Georgia Tech and Research Scientist at Facebook AI Research (FAIR)

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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SQuINTing at VQA Models: Interrogating VQA Models with Sub-Questions

Existing VQA datasets contain questions with varying levels of complexit...
01/20/2020 ∙ by Ramprasaath R. Selvaraju, et al. ∙ 44 ∙ 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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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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Trick or TReAT: Thematic Reinforcement for Artistic Typography

An approach to make text visually appealing and memorable is semantic re...
03/19/2019 ∙ by Purva Tendulkar, et al. ∙ 23 ∙ 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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12-in-1: Multi-Task Vision and Language Representation Learning

Much of vision-and-language research focuses on a small but diverse set ...
12/05/2019 ∙ by Jiasen Lu, 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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Lemotif: Abstract Visual Depictions of your Emotional States in Life

We present Lemotif. Lemotif generates a motif for your emotional life. Y...
03/18/2019 ∙ by Devi Parikh, 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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Cycle-Consistency for Robust Visual Question Answering

Despite significant progress in Visual Question Answering over the years...
02/15/2019 ∙ by Meet Shah, 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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Do Explanations make VQA Models more Predictable to a Human?

A rich line of research attempts to make deep neural networks more trans...
10/29/2018 ∙ by Arjun Chandrasekaran, et al. ∙ 13 ∙ 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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RUBi: Reducing Unimodal Biases in Visual Question Answering

Visual Question Answering (VQA) is the task of answering questions about...
06/24/2019 ∙ by Rémi Cadene, et al. ∙ 10 ∙ 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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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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Fashion++: Minimal Edits for Outfit Improvement

Given an outfit, what small changes would most improve its fashionabilit...
04/19/2019 ∙ by Wei-Lin Hsiao, 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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Active Learning for Visual Question Answering: An Empirical Study

We present an empirical study of active learning for Visual Question Ans...
11/06/2017 ∙ by Xiao Lin, et al. ∙ 0 ∙ share

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Cooperative Learning with Visual Attributes

Learning paradigms involving varying levels of supervision have received...
05/16/2017 ∙ by Tanmay Batra, 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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Context-aware Captions from Context-agnostic Supervision

We introduce an inference technique to produce discriminative context-aw...
01/11/2017 ∙ by Ramakrishna Vedantam, et al. ∙ 0 ∙ share

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Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning

Attention-based neural encoder-decoder frameworks have been widely adopt...
12/06/2016 ∙ by Jiasen Lu, 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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Deep Learning the City : Quantifying Urban Perception At A Global Scale

Computer vision methods that quantify the perception of urban environmen...
08/05/2016 ∙ by Abhimanyu Dubey, 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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Understanding Image Virality

Virality of online content on social networking websites is an important...
03/08/2015 ∙ by Arturo Deza, et al. ∙ 0 ∙ share

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Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks

Artificial agents today can answer factual questions. But they fall shor...
02/21/2015 ∙ by Xiao Lin, et al. ∙ 0 ∙ share

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Image Specificity

For some images, descriptions written by multiple people are consistent ...
02/16/2015 ∙ by Mainak Jas, et al. ∙ 0 ∙ share

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Collecting Image Description Datasets using Crowdsourcing

We describe our two new datasets with images described by humans. Both t...
11/12/2014 ∙ by Ramakrishna Vedantam, et al. ∙ 0 ∙ share

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Devi Parikh

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