Methods › Computer Vision › Image Segmentation Models › HANet

Height-driven Attention Network

HANet

5 papers tagged archive 2025-07-28

Introduced by Sungha Choi et al. in Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Height-driven Attention Network, or HANet, is a general add-on module for improving semantic segmentation for urban-scene images. It emphasizes informative features or classes selectively according to the vertical position of a pixel. The pixel-wise class distributions are significantly different from each other among horizontally segmented sections in the urban-scene images. Likewise, urban-scene images have their own distinct characteristics, but most semantic segmentation networks do not reflect such unique attributes in the architecture. The proposed network architecture incorporates the capability exploiting the attributes to handle the urban scene dataset effectively.

PaperSource

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

13 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Segmentation2
Semantic Segmentation2
Autonomous Driving1
Change Detection1
Crowd Counting1
Deep Learning1
Image Segmentation1
Retrieval1
Scene Segmentation1
Text Matching1
Text Retrieval1
Video-Text Retrieval1
text similarity1

Usage over time archive 2025-07-28

Papers per year tagged with HANet: 2020 to 2024, peak 3 3 0 2020: 1 paper 2020 2021: 3 papers 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Segmentation Models

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