Despite previous DETR-like methods having performed successfully in generic
object detection, tiny object detection is still a challenging task for them
since the positional information of object queries is not customized for
detecting tiny objects, whose scale is extraordinarily smaller than general
objects. Also, DETR-like methods using a fixed number of queries make them
unsuitable for aerial datasets, which only contain tiny objects, and the
numbers of instances are imbalanced between different images. Thus, we present
a simple yet effective model, named DQ-DETR, which consists of three different
components: categorical counting module, counting-guided feature enhancement,
and dynamic query selection to solve the above-mentioned problems. DQ-DETR uses
the prediction and density maps from the categorical counting module to
dynamically adjust the number of object queries and improve the positional
information of queries. Our model DQ-DETR outperforms previous CNN-based and
DETR-like methods, achieving state-of-the-art mAP 30.2% on the AI-TOD-V2
dataset, which mostly consists of tiny objects.

我们提出了一种名为 DQ-DETR 的简单而有效的模型，其中包括三个不同的组件：分类计数模块、计数引导的特征增强和动态查询选择，以解决 DET-R 类型方法在检测微小对象方面的挑战。DQ-DETR 使用分类计数模块的预测和密度图动态调整物体查询的数量，并改善查询的位置信息。我们的模型 DQ-DETR 在以微小对象为主的 AI-TOD-V2 数据集上实现了 30.2% 的最优 mAP，优于以前基于 CNN 和 DET-R 类型方法的性能。

DQ-DETR：用于微小物体检测的具有动态查询的 DETR

DQ-DETR: DETR with Dynamic Query for Tiny Object Detection

Contextual Text Block Detection (CTBD) is the task of identifying coherent
text blocks within the complexity of natural scenes. Previous methodologies
have treated CTBD as either a visual relation extraction challenge within
computer vision or as a sequence modeling problem from the perspective of
natural language processing. We introduce a new framework that frames CTBD as a
graph generation problem. This methodology consists of two essential
procedures: identifying individual text units as graph nodes and discerning the
sequential reading order relationships among these units as graph edges.
Leveraging the cutting-edge capabilities of DQ-DETR for node detection, our
framework innovates further by integrating a novel mechanism, a Dynamic
Relation Transformer (DRFormer), dedicated to edge generation. DRFormer
incorporates a dual interactive transformer decoder that deftly manages a
dynamic graph structure refinement process. Through this iterative process, the
model systematically enhances the graph's fidelity, ultimately resulting in
improved precision in detecting contextual text blocks. Comprehensive
experimental evaluations conducted on both SCUT-CTW-Context and ReCTS-Context
datasets substantiate that our method achieves state-of-the-art results,
underscoring the effectiveness and potential of our graph generation framework
in advancing the field of CTBD.

通过将 Contextual Text Block Detection 任务作为图生成问题，利用 DQ-DETR 和 Dynamic Relation Transformer 等先进技术，该研究提出了一种图生成框架，能够以高效准确的方式检测上下文文本块，取得了最先进的结果。