OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation

Ganlong Zhao, Guanbin Li, Weikai Chen, Yizhou Yu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 16296-16306

Recent advances in Iterative Vision-and-Language Navigation(IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end we propose OVER-NAV which aims to go over and beyond the current arts of IVLN techniques. In particular we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data we further introduce a structured representation coded Omnigraph to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments.