{"url":"/method/hanet","slug":"hanet","name":"HANet","full_name":"Height-driven Attention Network","full_name_withheld":false,"description_markdown":"**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.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks","paper":"/paper/cars-cant-fly-up-in-the-sky-improving-urban","first_author":"Sungha Choi","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/cars-cant-fly-up-in-the-sky-improving-urban"},"source":{"url":"https://arxiv.org/abs/2003.05128v3","title":"Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Segmentation Models","url":"/methods/category/image-segmentation-models","pwc_aliases":[]}],"n_papers_tagged":5,"archive_num_papers":5,"papers_newest_first":[{"paper":"/paper/hanet-a-hierarchical-attention-network-for","title":"HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images","date":"2024-04-14","arxiv_id":"2404.09178","n_code_links":1,"syntology":null},{"paper":"/paper/semantic-segmentation-for-urban-scene-images","title":"Semantic Segmentation for Urban-Scene Images","date":"2021-10-20","arxiv_id":"2110.13813","n_code_links":1,"syntology":null},{"paper":"/paper/hanet-hierarchical-alignment-networks-for","title":"HANet: Hierarchical Alignment Networks for Video-Text Retrieval","date":"2021-07-26","arxiv_id":"2107.12059","n_code_links":1,"syntology":null},{"paper":null,"title":"Hybrid attention network based on progressive embedding scale-context for crowd counting","date":"2021-06-04","arxiv_id":"2106.02324","n_code_links":0,"syntology":null},{"paper":"/paper/cars-cant-fly-up-in-the-sky-improving-urban","title":"Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks","date":"2020-03-11","arxiv_id":"2003.05128","n_code_links":1,"syntology":null}],"papers_shown":5,"tasks":[{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":2},{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":1},{"task":"/task/change-detection","name":"Change Detection","papers":1},{"task":"/task/crowd-counting","name":"Crowd Counting","papers":1},{"task":"/task/deep-learning","name":"Deep Learning","papers":1},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":1},{"task":"/task/retrieval","name":"Retrieval","papers":1},{"task":"/task/scene-segmentation","name":"Scene Segmentation","papers":1},{"task":"/task/text-matching","name":"Text Matching","papers":1},{"task":"/task/text-retrieval","name":"Text Retrieval","papers":1},{"task":"/task/video-text-retrieval","name":"Video-Text Retrieval","papers":1},{"task":"/task/text-similarity","name":"text similarity","papers":1}],"tasks_shown":13,"n_tasks":13,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":3},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/hanet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}