{"url":"/method/fcanet","slug":"fcanet","name":"FcaNet","full_name":"Frequency channel attention networks","full_name_withheld":false,"description_markdown":"FCANet contains a novel multi-spectral channel attention module. Given an input feature map $X \\in \\mathbb{R}^{C \\times H \\times W}$, multi-spectral channel attention first splits $X$ into many parts $x^{i} \\in \\mathbb{R}^{C' \\times H \\times W}$. Then it applies a 2D DCT to each part $x^{i}$. Note that a 2D DCT can use pre-processing results to reduce computation. After processing each part,  all results are concatenated into a vector. Finally, fully connected layers, ReLU activation and a sigmoid are used to get the attention vector as in an SE block. This can be formulated as:\r\n\\begin{align}\r\n    s = F_\\text{fca}(X, \\theta) & = \\sigma (W_{2} \\delta (W_{1}[(\\text{DCT}(\\text{Group}(X)))]))\r\n\\end{align}\r\n\\begin{align}\r\n    Y & = s  X\r\n\\end{align}\r\nwhere $\\text{Group}(\\cdot)$ indicates dividing the input into many groups and $\\text{DCT}(\\cdot)$ is the 2D discrete cosine transform. \r\n\r\nThis work based on information compression and discrete cosine transforms achieves excellent performance on the classification task.","description_state":"present","introduced_year":null,"introduced_by":{"title":"FcaNet: Frequency Channel Attention Networks","paper":"/paper/fcanet-frequency-channel-attention-networks","first_author":"Zequn Qin","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/fcanet-frequency-channel-attention-networks"},"source":{"url":"https://arxiv.org/abs/2012.11879v4","title":"FcaNet: Frequency Channel Attention Networks","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Attention Mechanisms","url":"/methods/category/attention-mechanisms","pwc_aliases":["attention-mechanisms-1"]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/orthonets-orthogonal-channel-attention","title":"OrthoNets: Orthogonal Channel Attention Networks","date":"2023-11-06","arxiv_id":"2311.03071","n_code_links":1,"syntology":null},{"paper":"/paper/fcanet-frequency-channel-attention-networks","title":"FcaNet: Frequency Channel Attention Networks","date":"2020-12-22","arxiv_id":"2012.11879","n_code_links":7,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/image-classification","name":"image-classification","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":6,"n_tasks":6,"usage_by_year":[{"year":"2020","papers":1},{"year":"2023","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/fcanet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}