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 …
A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities
Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …
environment has become essential for many countries' sustainable development. As various …
Segment anything in high quality
Abstract The recent Segment Anything Model (SAM) represents a big leap in scaling up
segmentation models, allowing for powerful zero-shot capabilities and flexible prompting …
segmentation models, allowing for powerful zero-shot capabilities and flexible prompting …
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 …
SegFormer: Simple and efficient design for semantic segmentation with transformers
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework
which unifies Transformers with lightweight multilayer perceptron (MLP) decoders …
which unifies Transformers with lightweight multilayer perceptron (MLP) decoders …
Semi-supervised semantic segmentation with cross pseudo supervision
In this paper, we study the semi-supervised semantic segmentation problem via exploring
both labeled data and extra unlabeled data. We propose a novel consistency regularization …
both labeled data and extra unlabeled data. We propose a novel consistency regularization …
RingMo: A remote sensing foundation model with masked image modeling
Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …
Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d
The goal of perception for autonomous vehicles is to extract semantic representations from
multiple sensors and fuse these representations into a single “bird's-eye-view” coordinate …
multiple sensors and fuse these representations into a single “bird's-eye-view” coordinate …
Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation
Point clouds can be represented in many forms (views), typically, point-based sets, voxel-
based cells or range-based images (ie, panoramic view). The point-based view is …
based cells or range-based images (ie, panoramic view). The point-based view is …