As drone technology advances, using unmanned aerial vehicles for aerial
surveys has become the dominant trend in modern low-altitude remote sensing.
The surge in aerial video data necessitates accurate prediction for future
scenarios and motion states of the interested target, particularly in
applications like traffic management and disaster response. Existing video
prediction methods focus solely on predicting future scenes (video frames),
suffering from the neglect of explicitly modeling target's motion states, which
is crucial for aerial video interpretation. To address this issue, we introduce
a novel task called Target-Aware Aerial Video Prediction, aiming to
simultaneously predict future scenes and motion states of the target. Further,
we design a model specifically for this task, named TAFormer, which provides a
unified modeling approach for both video and target motion states.
Specifically, we introduce Spatiotemporal Attention (STA), which decouples the
learning of video dynamics into spatial static attention and temporal dynamic
attention, effectively modeling the scene appearance and motion. Additionally,
we design an Information Sharing Mechanism (ISM), which elegantly unifies the
modeling of video and target motion by facilitating information interaction
through two sets of messenger tokens. Moreover, to alleviate the difficulty of
distinguishing targets in blurry predictions, we introduce Target-Sensitive
Gaussian Loss (TSGL), enhancing the model's sensitivity to both target's
position and content. Extensive experiments on UAV123VP and VisDroneVP (derived
from single-object tracking datasets) demonstrate the exceptional performance
of TAFormer in target-aware video prediction, showcasing its adaptability to
the additional requirements of aerial video interpretation for target
awareness.

无人机技术的不断进步使得使用无人机进行高空遥感成为现代低空遥感的主导趋势。随着航拍视频数据的激增，对于感兴趣目标未来场景和运动状态的准确预测尤为重要，尤其在交通管理和灾难响应等应用领域。现有的视频预测方法仅关注预测未来场景，忽视了目标运动状态的显式建模，这对于航拍视频解释来说至关重要。针对该问题，我们引入了一项新颖的任务，命名为目标感知航拍视频预测，旨在同时预测未来场景和目标的运动状态。此外，我们设计了一个专门针对该任务的模型，名为 TAFormer，它提供了一个统一的建模方法，可以同时建模视频和目标的运动状态。具体而言，我们引入了时空关注机制（STA），将视频动态学习分解为空间静态关注和时间动态关注，有效建模场景外观和运动。另外，我们设计了一种信息共享机制（ISM），通过两组信使令牌促进信息交互，优雅地统一了对视频和目标运动的建模。另外，为了减轻模糊预测中目标识别的困难，我们引入了目标敏感高斯损失（TSGL），增强了模型对目标位置和内容的敏感性。在 UAV123VP 和 VisDroneVP 等数据集上进行了大量实验证明了 TAFormer 在目标感知视频预测方面的卓越性能，展示了其适应航拍视频解释的额外要求。

TAFormer：面向航拍场景中视频和运动联合预测的统一目标感知 Transformer

TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint  Prediction in Aerial Scenes

Considering a conversation thread, stance classification aims to identify the
opinion (e.g. agree or disagree) of replies towards a given target. The target
of the stance is expected to be an essential component in this task, being one
of the main factors that make it different from sentiment analysis. However, a
recent study shows that a target-oblivious model outperforms target-aware
models, suggesting that targets are not useful when predicting stance. This
paper re-examines this phenomenon for rumour stance classification (RSC) on
social media, where a target is a rumour story implied by the source tweet in
the conversation. We propose adversarial attacks in the test data, aiming to
assess the models robustness and evaluate the role of the data in the models
performance. Results show that state-of-the-art models, including approaches
that use the entire conversation thread, overly relying on superficial signals.
Our hypothesis is that the naturally high occurrence of target-independent
direct replies in RSC (e.g. "this is fake" or just "fake") results in the
impressive performance of target-oblivious models, highlighting the risk of
target instances being treated as noise during training.

本文研究了社交媒体中谣言立场分类（RSC）问题中，目标感知模型与无目标模型的表现。结果发现，模型的表现过度依赖表面信号，高频的目标无关回复将目标感知模型的性能掩盖，加大了训练中目标实例被视为噪声的风险。