Recently, much research has been published for detecting when a classification neural network is presented with data that does not fit into one of the class labels the network learned at train time.

At present, deep learning has achieved great success in the field of object detection. To ensure that positive samples in the image are not missed, most deep-learning object detection methods set many ...

Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. However, existing approaches ...

In this paper, we present a review of deep-learning object detection from a camou-flaged perspective. We proposed a discriminative context-aware network called “Di-CANet.” In consideration of the ...

RetinaNet is a single-stage object detection network introduced by Facebook AI Research in 2017. It addresses class imbalance in dense object detectors through its key innovation, Focal Loss.

1. Introduction Automated object detection from visual images is important for many kinds of smart city applications, such as those involving camera-based monitoring or surveillance of areas within a ...

A technical paper titled “TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers” was published by researchers at ETH Zurich. Abstract: “This ...