Object Detection is the task of classification and localization of objects in an image or video.
It has gained prominence in recent years due to its widespread applications. This article …

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

Real-time object detection is one of the most important research topics in computer vision.
As new approaches regarding architecture optimization and training optimization are …

Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing
objects from an open set of categories in diverse environments. One way to address this …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

This paper presents a new vision Transformer, called Swin Transformer, that capably serves
as a general-purpose backbone for computer vision. Challenges in adapting Transformer …

In this paper, we propose a novel query design for the transformer-based object detection. In
previous transformer-based detectors, the object queries are a set of learned embeddings …

Building instance segmentation models that are data-efficient and can handle rare object
categories is an important challenge in computer vision. Leveraging data augmentations is a …

We show that the YOLOv4 object detection neural network based on the CSP approach,
scales both up and down and is applicable to small and large networks while maintaining …

Detecting tiny objects is a very challenging problem since a tiny object only contains a few
pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfactory …