As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for develo** new …

Salient object detection, which simulates human visual perception in locating the most
significant object (s) in a scene, has been widely applied to various computer vision tasks …

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for
object detection. While almost all state-of-the-art object detectors utilize predefined anchors …

This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF)
architecture for RGB-D salient object detection. Existing models usually treat RGB and depth …

Remote sensing image scene classification is an active and challenging task driven by
many applications. More recently, with the advances of deep learning models especially …

The High-Resolution Transformer (HRFormer) can maintain high-resolution representation
and share global receptive fields. It is friendly towards salient object detection (SOD) in …

Abstract Deep Neural Networks (DNNs) have substantially improved the state-of-the-art in
salient object detection. However, training DNNs requires costly pixel-level annotations. In …

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as
independent information and design separate networks for feature extraction from each …

Object detection, including objectness detection (OD), salient object detection (SOD), and
category-specific object detection (COD), is one of the most fundamental yet challenging …

The superiority of deeply learned pedestrian representations has been reported in very
recent literature of person re-identification (re-ID). In this article, we consider the more …