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Understanding spatial relations (e.g., "laptop on table") in visual input is important for both humans and robots.
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Pascal Matsakis, James M Keller, Laurent Wendling, Jonathon Marjamaa, and Ozy Sjahputera · 2001
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Marjorie Skubic, Dennis Perzanowski, Samuel Blisard, Alan Schultz, William Adams, Magda Bugajska, and Derek Brock · 2004
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Reinhard Moratz and Thora Tenbrink · 2006
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Grounding spatial relations for human-robot interaction
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Exploiting depth camera for 3d spatial relationship interpretation
Jun Ye and Kien A Hua · 2013
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Grounding spatial relations in natural language by fuzzy representation for human-robot interaction
Jiacheng Tan, Zhaojie Ju, and Honghai Liu · 2014
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Learning spatial knowledge for text to 3d scene generation
Angel Chang, Manolis Savva, and Christopher D Manning · 2014
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Grounding spatial relations for outdoor robot navigation
Abdeslam Boularias, Felix Duvallet, Jean Oh, and Anthony Stentz · 2015
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Learning the spatial semantics of manipulation actions through preposition grounding
Konstantinos Zampogiannis, Yezhou Yang, Cornelia Fermüller, and Yiannis Aloimonos · 2015
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The ycb object and model set: Towards common benchmarks for manipulation research
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Ruichi Yu, Ang Li, Vlad I. Morariu, and Larry S. Davis · 2017
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Weakly-supervised learning of visual relations
Julia Peyre, Josef Sivic, Ivan Laptev, and Cordelia Schmid · 2017
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Ai2-thor: An interactive 3d environment for visual ai
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Unlearn dataset bias in natural language inference by fitting the residual
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