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 …
Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …
order to achieve robust and accurate scene understanding, autonomous vehicles are …
Multi-interactive feature learning and a full-time multi-modality benchmark for image fusion and segmentation
Multi-modality image fusion and segmentation play a vital role in autonomous driving and
robotic operation. Early efforts focus on boosting the performance for only one task, eg …
robotic operation. Early efforts focus on boosting the performance for only one task, eg …
GMNet: Graded-feature multilabel-learning network for RGB-thermal urban scene semantic segmentation
Semantic segmentation is a fundamental task in computer vision, and it has various
applications in fields such as robotic sensing, video surveillance, and autonomous driving. A …
applications in fields such as robotic sensing, video surveillance, and autonomous driving. A …
Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and …
Image fusion aims to integrate complementary characteristics of source images into a single
fused image that better serves human visual observation and machine vision perception …
fused image that better serves human visual observation and machine vision perception …
CMX: Cross-modal fusion for RGB-X semantic segmentation with transformers
Scene understanding based on image segmentation is a crucial component of autonomous
vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting …
vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting …
Sne-roadseg: Incorporating surface normal information into semantic segmentation for accurate freespace detection
Freespace detection is an essential component of visual perception for self-driving cars. The
recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly …
recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly …
Deep multimodal fusion for semantic image segmentation: A survey
Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …
understanding tasks. However, in some complex environments or under challenging …
Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation
S Zhang, J Zhang, B Tian, T Lukasiewicz, Z Xu - Medical Image Analysis, 2023 - Elsevier
Semi-supervised learning has a great potential in medical image segmentation tasks with a
few labeled data, but most of them only consider single-modal data. The excellent …
few labeled data, but most of them only consider single-modal data. The excellent …
ABMDRNet: Adaptive-weighted bi-directional modality difference reduction network for RGB-T semantic segmentation
Semantic segmentation models gain robustness against poor lighting conditions by virtue of
complementary information from visible (RGB) and thermal images. Despite its importance …
complementary information from visible (RGB) and thermal images. Despite its importance …