With the increasing importance of video data in real-world applications,
there is a rising need for efficient object detection methods that utilize
temporal information. While existing video object detection (VOD) techniques
employ various strategies to address this challenge, they typically depend on
locally adjacent frames or randomly sampled images within a clip. Although
recent Transformer-based VOD methods have shown promising results, their
reliance on multiple inputs and additional network complexity to incorporate
temporal information limits their practical applicability. In this paper, we
propose a novel approach to single image object detection, called Context
Enhanced TRansformer (CETR), by incorporating temporal context into DETR using
a newly designed memory module. To efficiently store temporal information, we
construct a class-wise memory that collects contextual information across data.
Additionally, we present a classification-based sampling technique to
selectively utilize the relevant memory for the current image. In the testing,
We introduce a test-time memory adaptation method that updates individual
memory functions by considering the test distribution. Experiments with CityCam
and ImageNet VID datasets exhibit the efficiency of the framework on various
video systems. The project page and code will be made available at:
this https URL

我们提出了一种新颖的单图像目标检测方法，称为 Context Enhanced TRansformer (CETR)，通过将时间上下文引入 DETR 并使用新设计的内存模块。