D2FANet：基于双域特征聚合网络的视频目标检测增强
D2FANet: Enhancing Video Object Detection with Dual-Domain Feature Aggregation Network
将频域特征聚合引入视频目标检测，通过分解高低频分布捕捉周期性运动模式，并与时空域重要性引导聚合协同，为视频时序建模提供新思路。
原文依据 the first work to introduce the frequency-domain feature aggregation into the video object detection task
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论准确捕捉和聚合时空信息已成为视频目标检测的关键。先前方法主要在时空域进行特征聚合，对所有区域一视同仁，既忽略了它们的相对重要性，也忽略了刻画周期性运动模式的频率特性。这限制了这些方法捕捉动态交互和适应复杂场景变化的能力。本文提出一种新颖的双域特征聚合网络（D2FANet）用于视频目标检测，据我们所知，这是首个将频域特征聚合引入视频目标检测任务的工作。通过协同建模时空与频率信息，D2FANet增强了运动感知和时序一致性，从而提升检测精度。首先，我们开发了一个频域特征聚合模块，将帧特征分解为高频和低频分布，并通过聚合多尺度频率特征来强化目标查询表示。其次，我们设计了一个时空域特征聚合模块，利用重要性引导机制动态强调不同重要性的区域，并通过引导时空特征的聚合来强化目标查询表示。在ImageNet VID和EPIC-KITCHENS数据集上的实验表明，D2FANet达到了最优性能。
英文原文摘要 1527 字符
Accurately capturing and aggregating spatiotemporal information has become crucial for video object detection. Previous methods mainly perform feature aggregation in the spatiotemporal domain, treating all regions indiscriminately and overlooking both their relative importance and the frequency characteristics that capture periodic motion patterns. This limits the capability of these methods to capture dynamic interactions and adapt to complex scene variations. In this paper, we propose a novel Dual-Domain Feature Aggregation Network (D2FANet) for video object detection, which, to the best of our knowledge, is the first work to introduce the frequency-domain feature aggregation into the video object detection task. By collaboratively modeling spatiotemporal and frequency information, our D2FANet enhances motion awareness and temporal consistency, thereby improving detection accuracy. First, we develop a frequency-domain feature aggregation module that decomposes frame features into high- and low-frequency distributions and reinforces object query representations through aggregating multi-scale frequency features. Second, we design a spatiotemporal-domain feature aggregation module that leverages an importance guidance mechanism to dynamically emphasize regions of different importance and reinforces object query representations via guiding the aggregation of spatiotemporal features. Experiments on the ImageNet VID and EPIC-KITCHENS datasets demonstrate that D2FANet achieves state-of-the-art performance.