{"url":"/method/blendmask","slug":"blendmask","name":"BlendMask","full_name":"BlendMask","full_name_withheld":false,"description_markdown":"**BlendMask** is an [instance segmentation framework](https://paperswithcode.com/methods/category/instance-segmentation-models) built on top of the[ FCOS](https://paperswithcode.com/method/fcos) object detector. The bottom module uses either backbone or [FPN](https://paperswithcode.com/method/fpn) features to predict a set of bases. A single [convolution](https://paperswithcode.com/methods/category/convolutions) layer is added on top of the detection towers to produce attention masks along with each bounding box prediction. For each predicted instance, the [blender](https://paperswithcode.com/method/blender) crops the bases with its bounding box and linearly combine them according the learned attention maps. Note that the Bottom Module can take features either from ‘C’, or ‘P’ as the input.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2001.00309v3","title":"BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation","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":"Instance Segmentation Models","url":"/methods/category/instance-segmentation-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions","date":"2020-11-19","arxiv_id":"2011.09796","n_code_links":0,"syntology":null},{"paper":"/paper/blendmask-top-down-meets-bottom-up-for","title":"BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation","date":"2020-01-02","arxiv_id":"2001.00309","n_code_links":9,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":2},{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":2},{"task":null,"name":"GPU","papers":1},{"task":"/task/multi-task-learning","name":"Multi-Task Learning","papers":1},{"task":"/task/panoptic-segmentation","name":"Panoptic Segmentation","papers":1},{"task":"/task/real-time-instance-segmentation","name":"Real-time Instance Segmentation","papers":1}],"tasks_shown":7,"n_tasks":7,"usage_by_year":[{"year":"2020","papers":2}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/blendmask"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}