In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

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 …

Coarse-to-fine strategies have been extensively used for the architecture design of single
image deblurring networks. Conventional methods typically stack sub-networks with multi …

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 …

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

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

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-
scale modeling. However, spatial up-sampling operators (eg, interpolation, transposed …

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

Road extraction in remote sensing images is of great importance for a wide range of
applications. Because of the complex background, and high density, most of the existing …

Small object detection is a very challenging yet practical vision task. With deep network-
based methods, the contextual information of small objects may disappear when the network …