This report details the method of the winning entry of the AVDN Challenge in
ICCV 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 is
beneficial for navigation state tracking and robust action planning. TG-GAT
first leverages a graph-aware transformer to capture spatiotemporal
dependencies for more 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 2023, with 2.2% and 3.0% absolute improvements over the baseline
on SPL and SR metrics, respectively. The code is available at
this https URL

本研究提出了一种目标感知的图注意力变换器（TG-GAT）框架，用于无人机代理相关对话历史与航空观测的关联，以实现导航任务，并通过大型语言模型实现辅助视觉以及数据增强策略，有效提高导航性能。

面向航空视觉与对话导航的目标感知图形感知变压器

Target-Grounded Graph-Aware Transformer for Aerial Vision-and-Dialog  Navigation

Video compression artifact reduction aims to recover high-quality videos from
low-quality compressed videos. Most existing approaches use a single
neighboring frame or a pair of neighboring frames (preceding and/or following
the target frame) for this task. Furthermore, as frames of high quality overall
may contain low-quality patches, and high-quality patches may exist in frames
of low quality overall, current methods focusing on nearby peak-quality frames
(PQFs) may miss high-quality details in low-quality frames. To remedy these
shortcomings, in this paper we propose a novel end-to-end deep neural network
called non-local ConvLSTM (NL-ConvLSTM in short) that exploits multiple
consecutive frames. An approximate non-local strategy is introduced in
NL-ConvLSTM to capture global motion patterns and trace the spatiotemporal
dependency in a video sequence. This approximate strategy makes the non-local
module work in a fast and low space-cost way. Our method uses the preceding and
following frames of the target frame to generate a residual, from which a
higher quality frame is reconstructed. Experiments on two datasets show that
NL-ConvLSTM outperforms the existing methods.

本文提出了一种基于 ConvLSTM 的深度神经网络，称为非局部 ConvLSTM，以解决通过利用多个连续帧来降低视频压缩失真的问题，并在两个数据集上验证了其性能优于现有方法。