Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …

Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

We present SegFormer, a simple, efficient yet powerful semantic segmentation framework
which unifies Transformers with lightweight multilayer perceptron (MLP) decoders …

Deep learning-based semi-supervised learning (SSL) algorithms have led to promising
results in medical images segmentation and can alleviate doctors' expensive annotations by …

Visual salient object detection (SOD) aims at finding the salient object (s) that attract human
attention, while camouflaged object detection (COD) on the contrary intends to discover the …

Transformer-based models have been widely demonstrated to be successful in computer
vision tasks by modeling long-range dependencies and capturing global representations …

Semantic segmentation by using remote sensing images is an efficient method for
agricultural crop classification. Recent solutions in crop segmentation are mainly deep …

The contextual information plays a core role in semantic segmentation. As for video
semantic segmentation, the contexts include static contexts and motional contexts …

Deep learning based semantic segmentation solutions have yielded compelling results over
the preceding decade. They encompass diverse network architectures (FCN based or …