{"url":"/method/instaboost","slug":"instaboost","name":"InstaBoost","full_name":"InstaBoost","full_name_withheld":false,"description_markdown":"**InstaBoost** is a data augmentation technique for instance segmentation that utilises existing instance mask annotations.\r\n\r\nIntuitively in a small neighbor area of $(x_0, y_0, 1, 0)$, the probability map $P(x, y, s, r)$ should be high-valued since images are usually continuous and redundant in pixel level. Based on this, InstaBoost is a form of augmentation where we apply object jittering that randomly samples transformation tuples from the neighboring space of identity transform $(x_0, y_0, 1, 0)$ and paste the cropped object following affine transform $\\mathbf{H}$.","description_state":"present","introduced_year":null,"introduced_by":{"title":"InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting","paper":"/paper/instaboost-boosting-instance-segmentation-via","first_author":"Hao-Shu Fang","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/instaboost-boosting-instance-segmentation-via"},"source":{"url":"https://arxiv.org/abs/1908.07801v1","title":"InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/GothicAi/InstaBoost","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Data Augmentation","url":"/methods/category/image-data-augmentation","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":null,"title":"Instruction Following by Boosting Attention of Large Language Models","date":"2025-06-16","arxiv_id":"2506.13734","n_code_links":0,"syntology":null},{"paper":"/paper/instaboost-boosting-instance-segmentation-via","title":"InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting","date":"2019-08-21","arxiv_id":"1908.07801","n_code_links":3,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/data-augmentation","name":"Data Augmentation","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/instruction-following","name":"Instruction Following","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/prompt-engineering","name":"Prompt Engineering","papers":1},{"task":"/task/segmentation","name":"Segmentation","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1}],"tasks_shown":7,"n_tasks":7,"usage_by_year":[{"year":"2019","papers":1},{"year":"2025","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/instaboost"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}