{"url":"/method/effective-squeeze-and-excitation-block","slug":"effective-squeeze-and-excitation-block","name":"Effective Squeeze-and-Excitation Block","full_name":"Effective Squeeze-and-Excitation Block","full_name_withheld":false,"description_markdown":"**Effective Squeeze-and-Excitation Block** is an image model block based on squeeze-and-excitation, the difference being that one less FC layer is used. The authors note the SE module has a limitation: channel information loss due to dimension reduction. For avoiding high model complexity burden, two FC layers of the SE module need to reduce channel dimension. Specifically, while the first FC layer reduces input feature channels $C$ to $C/r$ using reduction ratio $r$, the second FC layer expands the reduced channels to original channel size $C$. As a result, this channel dimension reduction causes channel information loss. Therefore, effective SE (eSE) uses only one FC layer with $C$ channels instead of two FCs without channel dimension reduction, which maintains channel information.","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/1911.06667v6","title":"CenterMask : Real-Time Anchor-Free Instance Segmentation","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/youngwanLEE/CenterMask/blob/72147e8aae673fcaf4103ee90a6a6b73863e7fa1/maskrcnn_benchmark/modeling/backbone/vovnet.py#L108","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Model Blocks","url":"/methods/category/image-model-blocks","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/centermask-real-time-anchor-free-instance-1","title":"CenterMask : Real-Time Anchor-Free Instance Segmentation","date":"2019-11-15","arxiv_id":"1911.06667","n_code_links":8,"syntology":{"ran":1,"of":2,"unverified":1,"pointer_only":2}}],"papers_shown":1,"tasks":[{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/panoptic-segmentation","name":"Panoptic Segmentation","papers":1},{"task":"/task/real-time-object-detection","name":"Real-Time Object Detection","papers":1},{"task":"/task/real-time-instance-segmentation","name":"Real-time Instance Segmentation","papers":1},{"task":"/task/segmentation","name":"Segmentation","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/semi-supervised-instance-segmentation","name":"Semi-Supervised Instance Segmentation","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2019","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/effective-squeeze-and-excitation-block"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}