Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions
원문 초록
A long-term goal of AI research is to build intelligent agents that can communicate with humans in natural language, perceive the environment, and perform real-world tasks. Vision-and-Language Navigation (VLN) is a fundamental and interdisciplinary research topic towards this goal, and receives increasing attention from natural language processing, computer vision, robotics, and machine learning communities. In this paper, we review contemporary studies in the emerging field of VLN, covering tasks, evaluation metrics, methods, etc. Through structured analysis of current progress and challenges, we highlight the limitations of current VLN and opportunities for future work. This paper serves as a thorough reference for the VLN research community.
함께 살펴볼 논문
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NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments
A Navigation Framework Utilizing Vision-Language Models
Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation
FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models
Dynam3D: Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation
CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory
토론
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