Object recognition is among the fundamental tasks in the computer vision applications,
paving the path for all other image understanding operations. In every stage of progress in …

Recently, the vision transformer has achieved great success by pushing the state-of-the-art
of various vision tasks. One of the most challenging problems in the vision transformer is that …

Although current salient object detection (SOD) works have achieved significant progress,
they are limited when it comes to the integrity of the predicted salient regions. We define the …

Existing video understanding approaches, such as 3D convolutional neural networks and
Transformer-Based methods, usually process the videos in a clip-wise manner. Hence huge …

We explore the potential of pooling techniques on the task of salient object detection by
expanding its role in convolutional neural networks. In general, two pooling-based modules …

Weakly supervised semantic segmentation (WSSS) methods are often built on pixel-level
localization maps obtained from a classifier. However, training on class labels only …

Recent salient object detection (SOD) methods based on deep neural network have
achieved remarkable performance. However, most of existing SOD models designed for low …

Video segmentation—partitioning video frames into multiple segments or objects—plays a
critical role in a broad range of practical applications, from enhancing visual effects in movie …

We present a systematic study on a new task called dichotomous image segmentation (DIS),
which aims to segment highly accurate objects from natural images. To this end, we …

Salient object detection (SOD) in complex scenes and environments is a challenging
research topic. Most works focus on RGB-based SOD, which limits its performance of real …