Review the state-of-the-art technologies of semantic segmentation based on deep learning
The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …
information and predict the semantic category of each pixel from a given label set. With the …
Change detection methods for remote sensing in the last decade: A comprehensive review
Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …
detect and analyze changes occurring in the same geographical area over time, which has …
Segnext: Rethinking convolutional attention design for semantic segmentation
We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …
segmentation. Recent transformer-based models have dominated the field of se-mantic …
PIDNet: A real-time semantic segmentation network inspired by PID controllers
Two-branch network architecture has shown its efficiency and effectiveness in real-time
semantic segmentation tasks. However, direct fusion of high-resolution details and low …
semantic segmentation tasks. However, direct fusion of high-resolution details and low …
Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes
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 …
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …
Sam-adapter: Adapting segment anything in underperformed scenes
The emergence of large models, also known as foundation models, has brought significant
advancements to AI research. One such model is Segment Anything (SAM), which is …
advancements to AI research. One such model is Segment Anything (SAM), which is …
Transformer-based visual segmentation: A survey
Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …
segments or groups. This technique has numerous real-world applications, such as …
Topformer: Token pyramid transformer for mobile semantic segmentation
Although vision transformers (ViTs) have achieved great success in computer vision, the
heavy computational cost hampers their applications to dense prediction tasks such as …
heavy computational cost hampers their applications to dense prediction tasks such as …
Rethinking bisenet for real-time semantic segmentation
BiSeNet has been proved to be a popular two-stream network for real-time segmentation.
However, its principle of adding an extra path to encode spatial information is time …
However, its principle of adding an extra path to encode spatial information is time …
SAM Fails to Segment Anything?--SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
The emergence of large models, also known as foundation models, has brought significant
advancements to AI research. One such model is Segment Anything (SAM), which is …
advancements to AI research. One such model is Segment Anything (SAM), which is …