AWhat changed
Adapted dense convolutional features with atrous sampling and CRF boundary refinement; later added multi-rate spatial context pooling, image-level context and an encoder–decoder with atrous separable convolutions.
Scientific scope
Adapted dense convolutional features with atrous sampling and CRF boundary refinement; later added multi-rate spatial context pooling, image-level context and an encoder–decoder with atrous separable convolutions. Atrous convolution inherits wavelet/signal-processing and earlier neural precedents. Versions do not all use the same CRF: v3 and v3+ report results without that post-processing. Dense sampling, extra context and decoder refinement are version-specific contributions, not four independent inventions of segmentation.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Atrous convolution inherits wavelet/signal-processing and earlier neural precedents. Versions do not all use the same CRF: v3 and v3+ report results without that post-processing. Dense sampling, extra context and decoder refinement are version-specific contributions, not four independent inventions of segmentation.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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