The YOLO series has become the most popular framework for real-time object detection due
to its reasonable trade-off between speed and accuracy. However we observe that the …

We present GLEE in this work an object-level foundation model for locating and identifying
objects in images and videos. Through a unified framework GLEEaccomplishes detection …

Deriving reliable region-word alignment from image-text pairs is critical to learnobject-level
vision-language representations for open-vocabulary object detection. Existing methods …

In this paper we address the limitations of the DETR-based semi-supervised object detection
(SSOD) framework particularly focusing on the challenges posed by the quality of object …

This paper presents ClusterFormer, a universal vision model that is based on the Clustering
paradigm with TransFormer. It comprises two novel designs: 1) recurrent cross-attention …

Abstract Scene Graph Generation (SGG) is a challenging task of detecting objects and
predicting relationships between objects. After DETR was developed one-stage SGG …

Person search simultaneously detects and retrieves a query person from uncropped scene
images. Existing methods are either two-step or end-to-end. The former employs two …

Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-
time object detection owing to their effective balance between computational cost and …

Deep representation learning is a ubiquitous part of modern computer vision. While
Euclidean space has been the de facto standard manifold for learning visual …

Object detection from UAV (unmanned aerial vehicle) images is a crucial and challenging
task in the field of computer vision. The task suffers from the difficulties of small dense …