Don't let the information slip away

TL;DR

This paper introduces Association DETR, a novel object detection model that leverages background contextual information to improve detection accuracy, achieving state-of-the-art results on the COCO val2017 dataset.

Contribution

The paper proposes Association DETR, which incorporates background context into object detection, addressing a gap in existing models that focus mainly on foreground features.

Findings

Association DETR outperforms existing models on COCO val2017

Background context significantly improves detection accuracy

Achieves state-of-the-art results on benchmark dataset

Abstract

Real-time object detection has advanced rapidly in recent years. The YOLO series of detectors is among the most well-known CNN-based object detection models and cannot be overlooked. The latest version, YOLOv26, was recently released, while YOLOv12 achieved state-of-the-art (SOTA) performance with 55.2 mAP on the COCO val2017 dataset. Meanwhile, transformer-based object detection models, also known as DEtection TRansformer (DETR), have demonstrated impressive performance. RT-DETR is an outstanding model that outperformed the YOLO series in both speed and accuracy when it was released. Its successor, RT-DETRv2, achieved 53.4 mAP on the COCO val2017 dataset. However, despite their remarkable performance, all these models let information to slip away. They primarily focus on the features of foreground objects while neglecting the contextual information provided by the background. We believe that background information can significantly aid object detection tasks. For example, cars are more likely to appear on roads rather than in offices, while wild animals are more likely to be found in forests or remote areas rather than on busy streets. To address this gap, we propose an object detection model called Association DETR, which achieves state-of-the-art results compared to other object detection models on the COCO val2017 dataset.