Previous works have shown that increasing the window size for Transformer-based image
super-resolution models (eg, SwinIR) can significantly improve the model performance but …

Visual semantic segmentation aims at separating a visual sample into diverse blocks with
specific semantic attributes and identifying the category for each block, and it plays a crucial …

Appearance and motion are two important sources of information in video object
segmentation (VOS). Previous methods mainly focus on using simplex solutions, lowering …

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 …

Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …

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 …

Video saliency detection (VSD) aims at fast locating the most attractive
objects/things/patterns in a given video clip. Existing VSD-related works have mainly relied …

The ability to capture inter-frame dynamics has been critical to the development of video
salient object detection (VSOD). While many works have achieved great success in this field …

We present the first comprehensive video polyp segmentation (VPS) study in the deep
learning era. Over the years, developments in VPS are not moving forward with ease due to …

Existing video polyp segmentation (VPS) models typically employ convolutional neural
networks (CNNs) to extract features. However, due to their limited receptive fields, CNNs …