arXiv (OAI Expanded)
open access
A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images#615333
- Analysis of annotation requirements for training uncertainty-aware image segmentation models.#327119
- Semi-supervised Medical Image Segmentation via Perturbation-Aware Mutual Learning and Edge-Aware Uncertainty Loss for Accurate Anatomical Delineation#445028
- Latent-to-Latent Flow for Volumetric Stochastic Segmentation#984381
- Segmentation of Remotely Sensed Images Based on the Uncertainty of Multispectral Classification#642050
Record · ID 614366
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive