Review the state-of-the-art technologies of semantic segmentation based on deep learning
Y Mo, Y Wu, X Yang, F Liu, Y Liao - Neurocomputing, 2022 - Elsevier
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 …
Image segmentation using deep learning: A survey
Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …
applications such as scene understanding, medical image analysis, robotic perception …
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 …
Diffusionclip: Text-guided diffusion models for robust image manipulation
Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining
(CLIP) enables zero-shot image manipulation guided by text prompts. However, their …
(CLIP) enables zero-shot image manipulation guided by text prompts. However, their …
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 …
Encoder-based domain tuning for fast personalization of text-to-image models
Text-to-image personalization aims to teach a pre-trained diffusion model to reason about
novel, user provided concepts, embedding them into new scenes guided by natural …
novel, user provided concepts, embedding them into new scenes guided by natural …
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …
of practical applications, such as land cover mapping, urban change detection …
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 …
Headnerf: A real-time nerf-based parametric head model
In this paper, we propose HeadNeRF, a novel NeRF-based parametric head model that
integrates the neural radiance field to the parametric representation of the human head. It …
integrates the neural radiance field to the parametric representation of the human head. It …