{"url":"/method/channel-squeeze-and-spatial-excitation","slug":"channel-squeeze-and-spatial-excitation","name":"Channel Squeeze and Spatial Excitation","full_name":"Channel Squeeze and Spatial Excitation (sSE)","full_name_withheld":false,"description_markdown":"Inspired on the widely known [spatial squeeze and channel excitation (SE)](https://paperswithcode.com/method/squeeze-and-excitation-block) block, the sSE block performs channel squeeze and spatial excitation, to recalibrate the feature maps spatially and achieve more fine-grained image segmentation.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks","paper":"/paper/recalibrating-fully-convolutional-networks","first_author":"Abhijit Guha Roy","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/recalibrating-fully-convolutional-networks"},"source":{"url":"http://arxiv.org/abs/1808.08127v1","title":"Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/jlcsilva/segmentation_models.pytorch/blob/53c7f956ca557eb2cf386d28faacadf30ce4d0e2/segmentation_models_pytorch/base/modules.py#L117","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Attention Mechanisms","url":"/methods/category/attention-mechanisms","pwc_aliases":["attention-mechanisms-1"]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":"/paper/auco-resnet-an-end-to-end-network-for-covid","title":"AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath","date":"2022-03-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/encoder-decoder-architectures-for-clinically","title":"Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation","date":"2021-06-21","arxiv_id":"2106.11447","n_code_links":1,"syntology":null},{"paper":"/paper/recalibrating-fully-convolutional-networks","title":"Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks","date":"2018-08-23","arxiv_id":"1808.08127","n_code_links":5,"syntology":{"ran":2,"of":8,"unverified":6,"pointer_only":0}}],"papers_shown":3,"tasks":[{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":null,"name":"8k","papers":1},{"task":"/task/audio-classification","name":"Audio Classification","papers":1},{"task":"/task/covid-19-detection","name":"COVID-19 Diagnosis","papers":1},{"task":"/task/coronary-artery-segmentation","name":"Coronary Artery Segmentation","papers":1},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/dimensionality-reduction","name":"Dimensionality Reduction","papers":1},{"task":"/task/environmental-sound-classification","name":"Environmental Sound Classification","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/lesion-detection","name":"Lesion Detection","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/sound-classification","name":"Sound Classification","papers":1},{"task":"/task/feature-selection","name":"feature selection","papers":1},{"task":"/task/image-classification","name":"image-classification","papers":1}],"tasks_shown":15,"n_tasks":15,"usage_by_year":[{"year":"2018","papers":1},{"year":"2021","papers":1},{"year":"2022","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/channel-squeeze-and-spatial-excitation"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}