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
Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation	João Gustavo Atkinson Amorim, André Victória Matias, Allan Cerentini	2022	arxiv 2210.10641
The Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective	Arun Baskaran, Elizabeth J. Kautz, Aritra Chowdhary	2021	arxiv 2105.09729
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case	Sergey Nesteruk, Dmitrii Shadrin, Mariia Pukalchik	2021	arxiv 2102.12295
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss	Dongseok Shim, H. Jin Kim	2021	arxiv 2103.05902
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control	Zahra Gharaee, Karl Holmquist, Linbo He	2021	arxiv 2104.03807
Training-Free Out-Of-Distribution Segmentation With Foundation Models	Laith Nayal, Hadi Salloum, Ahmad Taha	2025	arxiv 2510.02909
Localized Region Guidance for Class Activation Mapping in WSSS	Ali Torabi, Sanjog Gaihre, MD Mahbubur Rahman	2025	arxiv 2509.12496
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions	Zach Eaton-Rosen, Felix Bragman, Sotirios Bisdas	2018	arxiv 1806.08640
Investigating Self-Supervised Methods for Label-Efficient Learning	Srinivasa Rao Nandam, Sara Atito, Zhenhua Feng	2024	arxiv 2406.17460
TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types