  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.
