In computer vision, a series of exemplary advances have been made in several areas
involving image classification, semantic segmentation, object detection, and image super …

Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …

Hospital emergency departments frequently receive lots of bone fracture cases, with
pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons …

Bounding box (bbox) regression is a fundamental task in computer vision. So far, the most
commonly used loss functions for bbox regression are the Intersection over Union (IoU) loss …

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

Complicated underwater environments bring new challenges to object detection, such as
unbalanced light conditions, low contrast, occlusion, and mimicry of aquatic organisms …

Multi-object tracking (MOT) is an important problem in computer vision which has a wide
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …

We develop a probabilistic interpretation of two-stage object detection. We show that this
probabilistic interpretation motivates a number of common empirical training practices. It …

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for
object detection. While almost all state-of-the-art object detectors utilize predefined anchors …

Abstract Localization Quality Estimation (LQE) is crucial and popular in the recent
advancement of dense object detectors since it can provide accurate ranking scores that …