{"url":"/method/global-local-attention-module","slug":"global-local-attention-module","name":"Global Local Attention Module","full_name":"Global Local Attention Module","full_name_withheld":false,"description_markdown":"The Global Local Attention Module (GLAM) is an image model block that attends to the feature map's channels and spatial dimensions locally, and also attends to the feature map's channels and spatial dimensions globally. The locally attended feature maps, globally attended feature maps, and the original feature maps are then fused through a weighted sum (with learnable weights) to obtain the final feature map.\r\n\r\nPaper:\r\n\r\nSong, C. H., Han, H. J., & Avrithis, Y. (2022). All the attention you need: Global-local, spatial-channel attention for image retrieval. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2754-2763).","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/2107.08000v1","title":"All the attention you need: Global-local, spatial-channel attention for image retrieval","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 Model Blocks","url":"/methods/category/image-model-blocks","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"DMAGaze: Gaze Estimation Based on Feature Disentanglement and Multi-Scale Attention","date":"2025-04-15","arxiv_id":"2504.11160","n_code_links":0,"syntology":null},{"paper":null,"title":"All the attention you need: Global-local, spatial-channel attention for image retrieval","date":"2021-07-16","arxiv_id":"2107.08000","n_code_links":0,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/all","name":"All","papers":1},{"task":"/task/disentanglement","name":"Disentanglement","papers":1},{"task":"/task/gaze-estimation","name":"Gaze Estimation","papers":1},{"task":"/task/head-pose-estimation","name":"Head Pose Estimation","papers":1},{"task":"/task/image-retrieval","name":"Image Retrieval","papers":1},{"task":"/task/pose-estimation","name":"Pose Estimation","papers":1},{"task":"/task/representation-learning","name":"Representation Learning","papers":1},{"task":"/task/retrieval","name":"Retrieval","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2021","papers":1},{"year":"2025","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/global-local-attention-module"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}