Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
| Autor: | Howard Chen, Alane Suhr, Yoav Artzi, Noah Snavely, Dipendra Misra |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Task (project management) Machine Learning (cs.LG) Human–computer interaction Position (vector) 11. Sustainability 0202 electrical engineering electronic engineering information engineering 0105 earth and related environmental sciences Computer Science - Computation and Language business.industry Touchdown Spatial intelligence Visual reasoning Object (computer science) Artificial Intelligence (cs.AI) 020201 artificial intelligence & image processing Artificial intelligence business Computation and Language (cs.CL) Natural language |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.1811.12354 |
| Popis: | We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the Touchdown task and dataset, where an agent must first follow navigation instructions in a real-life visual urban environment, and then identify a location described in natural language to find a hidden object at the goal position. The data contains 9,326 examples of English instructions and spatial descriptions paired with demonstrations. Empirical analysis shows the data presents an open challenge to existing methods, and qualitative linguistic analysis shows that the data displays richer use of spatial reasoning compared to related resources. Comment: arXiv admin note: text overlap with arXiv:1809.00786 |
| Databáze: | OpenAIRE |
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