What is recorded here?
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.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
Read the original work
- 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
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Liang-Chieh Chen; George Papandreou; Iasonas Kokkinos; Kevin Murphy; Alan L. Yuille; Florian Schroff; Hartwig Adam; Yukun Zhu
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.
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English catalogue narrative
DeepLab family: atrous context modeling and boundary refinement
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
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.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Liang-Chieh Chen; George Papandreou; Iasonas Kokkinos; Kevin Murphy; Alan L. Yuille; Florian Schroff; Hartwig Adam; Yukun Zhu
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.
Original sources
- 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