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arXiv 1908.04950cs.CVcs.AIcs.CLcs.LG

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

University of Cambridge(剑桥大学);Mila(米拉研究所);Université de Montréal(蒙特利尔大学)

Cătălina Cangea, Eugene Belilovsky, Pietro Liò, Aaron Courville

英文摘要:

Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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