Semantic Segmentation
Semantic segmentation, the task of assigning a semantic label to each pixel in an image, aims to achieve precise pixel-level scene understanding. Current research emphasizes improving accuracy and efficiency across diverse data modalities (RGB, depth, lidar, hyperspectral, and time series) and challenging conditions (low light, adverse weather, imbalanced datasets), often employing advanced architectures like transformers and diffusion models alongside innovative loss functions and training strategies. This field is crucial for numerous applications, including autonomous driving, medical image analysis, remote sensing, and robotics, driving advancements in both model robustness and interpretability.
Papers
Calculation of Femur Caput Collum Diaphyseal angle for X-Rays images using Semantic Segmentation
Muhammad Abdullah, Anne Querfurth, Deepak Bhatia, Mahdi Mantash
Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals
Oliver Hahn, Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth
Multi-Scale Representations by Varying Window Attention for Semantic Segmentation
Haotian Yan, Ming Wu, Chuang Zhang
Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models
Hedda Cohen Indelman, Elay Dahan, Angeles M. Perez-Agosto, Carmit Shiran, Doron Shaked, Nati Daniel
Semantic-Rearrangement-Based Multi-Level Alignment for Domain Generalized Segmentation
Guanlong Jiao, Chenyangguang Zhang, Haonan Yin, Yu Mo, Biqing Huang, Hui Pan, Yi Luo, Jingxian Liu
PV-S3: Advancing Automatic Photovoltaic Defect Detection using Semi-Supervised Semantic Segmentation of Electroluminescence Images
Abhishek Jha, Yogesh Rawat, Shruti Vyas
LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing
Tong Wang, Guanzhou Chen, Xiaodong Zhang, Chenxi Liu, Xiaoliang Tan, Jiaqi Wang, Chanjuan He, Wenlin Zhou