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Target-Grounded Graph-Aware Transformer for Aerial Vision-and-Dialog Navigation
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This report details the methods of the winning entry of the AVDN Challenge in ICCV CLVL 2023. The competition addresses the Aerial Navigation from Dialog History (ANDH) task, which requires a drone agent to associate dialog history with aerial observations to reach the destination. For better cross-modal grounding abilities of the drone agent, we propose a Target-Grounded Graph-Aware Transformer (TG-GAT) framework. Concretely, TG-GAT first leverages a graph-aware transformer to capture spatiotemporal dependency, which benefits navigation state tracking and robust action planning. In addition,an auxiliary visual grounding task is devised to boost the agent's awareness of referred landmarks. Moreover, a hybrid augmentation strategy based on large language models is utilized to mitigate data scarcity limitations. Our TG-GAT framework won the AVDN Challenge, with 2.2% and 3.0% absolute improvements over the baseline on SPL and SR metrics, respectively. The code is available at https://github.com/yifeisu/TG-GAT.
Forward citations
Cited by 2 Pith papers
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Parse, Search, and Confirmation: Training-Free Aerial Vision-and-Dialog Navigation with Chain-of-Thought Reasoning and Structured Spatial Memory
A three-stage parse-search-confirm MLLM pipeline plus structured spatial memory sets training-free SOTA on AVDN, matching or beating several supervised methods on ANDH and ANDH-Full.
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Deliberate Before You Fly: Vision-Guided Spatial Deliberation for UAV See-and-Reach Navigation
DBFly improves UAV see-and-reach success by adding explicit spatial reasoning steps (direction, diagnosis, maneuver, stop) before predicting waypoints, reporting a 25-point gain over the prior SOTA on the UAV-VLN-FOV ...
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