SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph

Understand

Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when complex relative positions and information alignments are involved, which poses a bottleneck in response quality.

• In this paper, we propose a Situated Conversation Agent Petrained with Multimodal Questions from INcremental Layout Graph (SPRING) with abilities of reasoning multi-hops spatial relations and connecting them with visual attributes in crowded situated scenarios.
• Specifically, we design two types of Multimodal Question Answering (MQA) tasks to pretrain the agent.
• All QA pairs utilized during pretraining are generated from novel Incremental Layout Graphs (ILG).