The core innovation lies in replacing the traditional DETR backbone with ConvNeXt, a convolutional neural network inspired by ...

Traditional object detection algorithms, including both two-stage models like Faster R-CNN and one-stage variants such as ...

Abstract: Bounding Box Regression (BBR) plays a critical role in object detection by refining the predicted location and size of objects to enhance model accuracy. This process involves adjusting the ...

Large-scale object detection datasets (e.g., MS-COCO ... In this paper, we propose a novel bounding box regression loss for learning bounding box transformation and localization variance together. Our ...

Abstract: In this work, we introduce a countermeasure method to neutralize the impact of adversarial patch attacks in object detection by filling bounding boxes possessing an objectness score below ...

The main challenge of monocular 3D object detection is the accurate localization of ... which combines the information flow from 2D-to-3D (3D bounding box proposal generation with a single 2D image) ...

However, this is true only for a few object categories. This is because most detection techniques depend on supervision in the form of instance-level bounding box annotations, demanding human labeling ...

The bounding box placement is accurate down to a single pixel, allowing Figure Eight customers to have the most accurate possible human-driven object detection for their Computer Vision models.