Search Results for author: Eric Kolve

Found 14 papers, 7 papers with code

Webly Supervised Concept Expansion for General Purpose Vision Models

no code implementations4 Feb 2022 Amita Kamath, Christopher Clark, Tanmay Gupta, Eric Kolve, Derek Hoiem, Aniruddha Kembhavi

This work presents an effective and inexpensive alternative: learn skills from supervised datasets, learn concepts from web image search, and leverage a key characteristic of GPVs: the ability to transfer visual knowledge across skills.

Human-Object Interaction Detection Image Retrieval +4

CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents

2 code implementations19 Oct 2021 Sam Powers, Eliot Xing, Eric Kolve, Roozbeh Mottaghi, Abhinav Gupta

In this work, we present CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package.

NetHack reinforcement-learning +1

ManipulaTHOR: A Framework for Visual Object Manipulation

1 code implementation CVPR 2021 Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi

Object manipulation is an established research domain within the robotics community and poses several challenges including manipulator motion, grasping and long-horizon planning, particularly when dealing with oft-overlooked practical setups involving visually rich and complex scenes, manipulation using mobile agents (as opposed to tabletop manipulation), and generalization to unseen environments and objects.

Learning Flexible Visual Representations via Interactive Gameplay

no code implementations ICLR 2021 Luca Weihs, Aniruddha Kembhavi, Kiana Ehsani, Sarah M Pratt, Winson Han, Alvaro Herrasti, Eric Kolve, Dustin Schwenk, Roozbeh Mottaghi, Ali Farhadi

A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making and socialization.

Decision Making Representation Learning

RoboTHOR: An Open Simulation-to-Real Embodied AI Platform

1 code implementation CVPR 2020 Matt Deitke, Winson Han, Alvaro Herrasti, Aniruddha Kembhavi, Eric Kolve, Roozbeh Mottaghi, Jordi Salvador, Dustin Schwenk, Eli VanderBilt, Matthew Wallingford, Luca Weihs, Mark Yatskar, Ali Farhadi

We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems.

Learning Generalizable Visual Representations via Interactive Gameplay

no code implementations17 Dec 2019 Luca Weihs, Aniruddha Kembhavi, Kiana Ehsani, Sarah M Pratt, Winson Han, Alvaro Herrasti, Eric Kolve, Dustin Schwenk, Roozbeh Mottaghi, Ali Farhadi

A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making, and socialization.

Decision Making Representation Learning

Visual Semantic Planning using Deep Successor Representations

no code implementations ICCV 2017 Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, Ali Farhadi

A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world.

Imitation Learning reinforcement Learning

Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning

2 code implementations16 Sep 2016 Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, Ali Farhadi

To address the second issue, we propose AI2-THOR framework, which provides an environment with high-quality 3D scenes and physics engine.

3D Reconstruction Feature Engineering +3

A Diagram Is Worth A Dozen Images

1 code implementation24 Mar 2016 Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi

We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering.

Visual Question Answering

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