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

Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

Although perception systems have made remarkable advancements in recent years they still
rely on explicit human instruction or pre-defined categories to identify the target objects …

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 …

Using light-weight architectures or reasoning on low-resolution images, recent methods
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …

Object detection is a well-known task in the field of computer vision, especially the small
target detection problem that has aroused great academic attention. In order to improve the …

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

Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary
categories of text-based descriptions, which popularizes the segmentation system to more …

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with
an encoder-decoder architecture. The encoder progressively reduces the spatial resolution …