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

In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

Open-world object detection (OWOD) is a challenging computer vision problem, where the
task is to detect a known set of object categories while simultaneously identifying unknown …

There are a huge number of features which are said to improve Convolutional Neural
Network (CNN) accuracy. Practical testing of combinations of such features on large …

Neural networks have enabled state-of-the-art approaches to achieve incredible results on
computer vision tasks such as object detection. However, such success greatly relies on …

Object detection has been dominated by anchor-based detectors for several years.
Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal …

FPN-based detectors have made significant progress in general object detection, eg, MS
COCO and CityPersons. However, these detectors fail in certain application scenarios, eg …

Understanding interactions between humans and objects is one of the fundamental
problems in visual classification and an essential step towards detailed scene …

We propose a novel two-stage detection method, D2Det, that collectively addresses both
precise localization and accurate classification. For precise localization, we introduce a …

Object detection is a crucial branch of computer vision that aims to locate and classify
objects in images. Using deep convolutional neural networks (CNNs) as the primary …