With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

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

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 …

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

Salient object segmentation, edge detection, and skeleton extraction are three contrasting
low-level pixel-wise vision problems, where existing works mostly focused on designing …

Object detection has achieved remarkable progress in the past decade. However, the
detection of oriented and densely packed objects remains challenging because of following …

Fast and accurate smoke detection is very important for reducing fire damage. Due to the
complexity and changeable nature of smoke scenes, existing smoke detection technology …

Abstract One-to-one (o2o) label assignment plays a key role for transformer based end-to-
end detection, and it has been recently introduced in fully convolutional detectors for …