Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

Object detection is a significant downstream task in computer vision. For the on-board edge
computing platforms, a giant model is difficult to achieve the real-time detection requirement …

One-stage object detection is commonly implemented by optimizing two sub-tasks: object
classification and localization, using heads with two parallel branches, which might lead to a …

The recently-developed DETR approach applies the transformer encoder and decoder
architecture to object detection and achieves promising performance. In this paper, we …

Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and
military domains. However, the high proportion of small objects in UAV images and the …

Recent advances in label assignment in object detection mainly seek to independently
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …

Accurately ranking the vast number of candidate detections is crucial for dense object
detectors to achieve high performance. Prior work uses the classification score or a …

One-stage detector basically formulates object detection as dense classification and
localization (ie, bounding box regression). The classification is usually optimized by Focal …

DETR is a recently proposed Transformer-based method which views object detection as a
set prediction problem and achieves state-of-the-art performance but demands extra-long …