The existing fusion-based RGB-D salient object detection methods usually adopt the
bistream structure to strike a balance in the fusion trade-off between RGB and depth (D) …

In this paper, we identify and address a serious design bias of existing salient object
detection (SOD) datasets, which unrealistically assume that each image should contain at …

This article proposes a domain adaptive learning framework based on physical model
feedback for underwater image enhancement. Underwater image enhancement involves …

Fall detection is drawing more attention from both academia and industry. The human body
occupies smaller space relative to the background in images, so the complex background …

Few-shot learning (FSL) aims to learn a model that can identify unseen classes using only a
few training samples from each class. Most of the existing FSL methods adopt a manually …

Though deep learning-based saliency detection methods have achieved gratifying
performance recently, the predicted saliency maps still suffer from the boundary challenge …

Salient object detection has witnessed rapid progress, despite most existing methods still
struggling in complex scenes, unfortunately. In this paper, we propose an efficient framework …

针对当前基于流形排序的显著性检测算法缺乏子空间信息的挖掘和节点间传播不准确的问题,
该文提出一种基于低秩背景约束与多线索传播的图像显著性检测算法. 融合颜色 …

Top-down, goal-driven visual saliency exerts a huge influence on the human visual system
for performing visual tasks. Text generations, like visual question answering (VQA) and …

This paper proposes a new approach to single depth image super-resolution (SR), based
upon a novel joint sparse coding model. A low-resolution color is used as a guide in the SR …