R2R (Room-to-Room)|自然语言处理数据集|计算机视觉数据集

R2R is a dataset for visually-grounded natural language navigation in real buildings. The dataset requires autonomous agents to follow human-generated navigation instructions in previously unseen buildings, as illustrated in the demo above. For training, each instruction is associated with a Matterport3D Simulator trajectory. 22k instructions are available, with an average length of 29 words. There is a test evaluation server for this dataset available at EvalAI.

AI搜集汇总

背景与挑战

背景概述

R2R数据集是一个用于视觉基础自然语言导航的数据集，包含22,000条人类生成的导航指令，每条指令平均长度为29个单词，并与Matterport3D模拟器轨迹相关联。数据集支持在未见过的建筑环境中进行导航，并提供了一个测试评估服务器。