Abstract
Multimodal reasoning is a process of understanding, integrating and inferring information across different data modalities. It has recently attracted surging academic attention as a benchmark for Artificial Intelligence (AI). Although there are various tasks for evaluating multimodal reasoning ability, they still have limitations. Lack of reasoning on hierarchical visual clues at different levels of granularity, e.g., local details and global context, is of little discussion, despite its frequent involvement in real scenarios. To bridge the gap, we introduce a novel and challenging task for multimodal reasoning, namely GeoGuess. Given a street view image, the task is to identify its location and provide a detailed explanation. A system that succeeds in GeoGuess should be able to detect tiny visual clues, perceive the broader landscape, and associate with vast geographic knowledge. Therefore, GeoGuess would require the ability to reason between hierarchical visual information and geogra