SOTA2 Research · papers

Papers for “semantic segmentation”

Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.

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20 indexed

Title	Authors	Year	Source
SM2C: Boost the Semi-supervised Segmentation for Medical Image by using Meta Pseudo Labels and Mixed Images	Yifei Wang, Chuhong Zhu	2024	arxiv 2403.16009
TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification	Qinying Liu, Wei Wu, Kecheng Zheng	2023	arxiv 2312.14149
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis	Hanrong Ye, Jason Kuen, Qing Liu	2023	arxiv 2311.03355
Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation	Fuchen Zheng, Quanjun Li, Weixuan Li	2024	arxiv 2409.07793
Do text-free diffusion models learn discriminative visual representations?	Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi	2023	arxiv 2311.17921
VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings	Andrea Carrara, Stavros Nousias, André Borrmann	2024	arxiv 2410.01336
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models	Changming Xiao, Qi Yang, Feng Zhou	2023	arxiv 2309.04109
Multi-Layer Feature Fusion with Cross-Channel Attention-Based U-Net for Kidney Tumor Segmentation	Fnu Neha, Arvind K. Bansal	2024	arxiv 2410.15472
LiOn-XA: Unsupervised Domain Adaptation via LiDAR-Only Cross-Modal Adversarial Training	Thomas Kreutz, Jens Lemke, Max Mühlhäuser	2024	arxiv 2410.15833
CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation

Latents of latents to delineate pixels: hybrid Matryoshka autoencoder-to-U-Net pairing for segmenting large medical images in GPU-poor and low-data regimes	Tahir Syed, Ariba Khan, Sawera Hanif	2025	arxiv 2502.08988