Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation

We propose a novel end-to-end trainable, deep, encoder-decoder architecture for single-pass semantic segmentation. Our approach is based on a cascaded architecture with feature-level long-range skip connections. The encoder incorporates the structure of ResNeXt's residual building blocks and ad...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition s. 6596 - 6605
Hlavní autoři: Bilinski, Piotr, Prisacariu, Victor
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2018
Témata:
Architecture
Computer architecture
Decoding
Fuses
Image segmentation
Semantics
Spatial resolution
ISSN:1063-6919
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:We propose a novel end-to-end trainable, deep, encoder-decoder architecture for single-pass semantic segmentation. Our approach is based on a cascaded architecture with feature-level long-range skip connections. The encoder incorporates the structure of ResNeXt's residual building blocks and adopts the strategy of repeating a building block that aggregates a set of transformations with the same topology. The decoder features a novel architecture, consisting of blocks, that (i) capture context information, (ii) generate semantic features, and (iii) enable fusion between different output resolutions. Crucially, we introduce dense decoder shortcut connections to allow decoder blocks to use semantic feature maps from all previous decoder levels, i.e. from all higher-level feature maps. The dense decoder connections allow for effective information propagation from one decoder block to another, as well as for multi-level feature fusion that significantly improves the accuracy. Importantly, these connections allow our method to obtain state-of-the-art performance on several challenging datasets, without the need of time-consuming multi-scale averaging of previous works.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00690