Heavy and Lightweight Deep Learning Models for Semantic Segmentation: A Survey
Semantic segmentation is an important computer vision task due to its numerous real-world applications such as autonomous driving, video surveillance, medical image analysis, robotics, augmented reality, among others, and its popularity increased with the development of deep learning approaches. We...
Saved in:
Main Authors: | Cristina Carunta, Alina Carunta, Calin-Adrian Popa |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10840225/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhanced CATBraTS for Brain Tumour Semantic Segmentation
by: Rim El Badaoui, et al.
Published: (2025-01-01) -
Enhancing Remote Sensing Semantic Segmentation Accuracy and Efficiency Through Transformer and Knowledge Distillation
by: Kang Zheng, et al.
Published: (2025-01-01) -
Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders
by: Thanat Payatsuporn, et al.
Published: (2025-01-01) -
Segment anything model for few-shot medical image segmentation with domain tuning
by: Weili Shi, et al.
Published: (2024-11-01) -
Semantic Tokenization-Based Mamba for Hyperspectral Image Classification
by: Ri Ming, et al.
Published: (2025-01-01)