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Moving Beyond the Turing Test with the Allen AI Science Challenge
Carissa Schoenick, Peter Clark, Oyvind Tafjord, Peter Turney, and Oren EtzioniCACM • 2016 The field of Artificial Intelligence has made great strides forward recently, for example AlphaGo's recent victory against the world champion Lee Sedol in the game of Go, leading to great optimism about the field. But are we really moving towards smarter…Much Ado About Time: Exhaustive Annotation of Temporal Data
Gunnar A. Sigurdsson, Olga Russakovsky, Ali Farhadi, Ivan Laptev, and Abhinav GuptaHCOMP • 2016 Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These techniques often implicitly rely on the fact that a new input…Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, and Ali FarhadiCVPR • 2016 In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term…PDFFigures 2.0: Mining Figures from Research Papers
Christopher Clark and Santosh DivvalaJCDL • 2016 Figures and tables are key sources of information in many scholarly documents. However, current academic search engines do not make use of figures and tables when semantically parsing documents or presenting document summaries to users. To facilitate these…Probabilistic Models for Learning a Semantic Parser Lexicon
Jayant KrishnamurthyNAACL • 2016 We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new application domain and the quality of the learned lexicon significantly affects both the…Question Answering via Integer Programming over Semi-Structured Knowledge
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Peter Clark, Oren Etzioni, and Dan RothIJCAI • 2016 Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non-trivial inference and knowledge that goes beyond factoid retrieval. Yet, most systems for this task are based on relatively shallow…Semantic Parsing to Probabilistic Programs for Situated Question Answering
Jayant Krishnamurthy, Oyvind Tafjord, and Aniruddha KembhaviEMNLP • 2016 Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present…Situation Recognition: Visual Semantic Role Labeling for Image Understanding
Mark Yatskar, Luke Zettlemoyer, and Ali FarhadiCVPR • 2016 This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears…Stating the Obvious: Extracting Visual Common Sense Knowledge
Mark Yatskar, Vicente Ordonez, and Ali FarhadiNAACL • 2016 Obtaining common sense knowledge using current information extraction techniques is extremely challenging. In this work, we instead propose to derive simple common sense statements from fully annotated object detection corpora such as the Microsoft Common…Toward a Taxonomy and Computational Models of Abnormalities in Images
Babak Saleh, Ahmed Elgammal, Jacob Feldman, and Ali FarhadiAAAI • 2016 The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has…