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AI2 NEWSLETTER | May 2019 | CVPR
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AI2 is back at CVPR in June 2019, this time to talk Common Sense AI—we will be presenting two papers that successfully demonstrate important elements of machine common sense in a real-world simulation for the first time. AI2’s PRIOR team will discuss AI agents that can communicate and cooperate together visually, and an AI agent that can teach itself how to navigate around in a new environment.

Both of these advances were created with the powerful AI2-THOR framework, a unique way to study and develop common sense for AI that goes beyond the limitations of text or robotics. AI2-THOR is a platform for building near-photorealistic simulated environments with real-world physics, textures, and object interactions.





AI2 will make a strong showing at CVPR with a total of eight accepted papers, four of which will have oral presentations. Learn more about some of our submissions in this special edition of the AI2 Newsletter.
 
SELECTED PAPERS


Two Body Problem: Collaborative Visual Task Completion
Unnat Jain*, Luca Weihs*, Eric Kolve, Mohammad Rastegari, Svetlana Lazebnik, A. Farhadi, Alexander G. Schwing, Aniruddha Kembhavi
[*equal contribution]


Collaboration is a necessary skill to perform tasks that are beyond a single agent’s capabilities. This paper argues that there are inherently visual aspects to collaboration that should be studied in visually rich environments. We investigate the problem of learning to collaborate directly from pixels using the AI2-THOR framework, and demonstrate the benefits of explicit and implicit modes of communication in performing visual tasks.


Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
Mitchell Wortsman, Kiana Ehsani, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi

Learning is continuous; after humans learn a new task, we keep learning about it while we perform the task. Learning how to learn is a critical skill that enables us to generalize our knowledge to new problems. In this paper, we challenge an AI agent to learn self-supervised, effective navigation around a room. Our method demonstrates major improvements for visual navigation in novel scenes.
 

ELASTIC: Improving CNNs with Instance Specific Scaling Policies
Huiyu Wang, Aniruddha Kembhavi, Ali Farhadi, Alan Loddon Yuille, Mohammad Rastegari

ELASTIC is a simple, efficient, and very effective approach to learn the scale variations (relative sizes) of objects in a given image purely from data. We demonstrate consistent improvement over the state-of-the-art-performance on multiple well-known image classification and segmentation challenges, without extra (sometimes with even lower) computational effort required.

From Recognition to Cognition: Visual Commonsense Reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi

Visual Commonsense Reasoning (VCR) is a compelling new challenge for cognition-level visual understanding in AI. Good performance on the VCR dataset will require a model to answer challenging visual questions using natural language, as well as to provide a rationale explaining why its answer is true.
 
See all of AI2's recent publications on our website →
 
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