{"url":"/method/dimfuse","slug":"dimfuse","name":"DimFuse","full_name":"Dimension-wise Fusion","full_name_withheld":false,"description_markdown":"**Dimension-wise Fusion** is an image model block that attempts to capture global information by combining features globally. It is an alternative to point-wise [convolution](https://paperswithcode.com/method/convolution). A point-wise convolutional layer applies $D$ point-wise kernels $\\mathbf{k}\\_p \\in \\mathbb{R}^{3D \\times 1 \\times 1}$ and performs $3D^2HW$ operations to combine dimension-wise representations of $\\mathbf{Y_{Dim}} \\in \\mathbb{R}^{3D \\times H \\times W}$ and produce an output $\\mathbf{Y} \\in \\mathbb{R}^{D \\times H \\times W}$. This is computationally expensive. Dimension-wise fusion is an alternative that can allow us to combine representations of $\\mathbf{Y\\_{Dim}}$ efficiently. As illustrated in the Figure to the right, it factorizes the point-wise convolution in two steps: (1) local fusion and (2) global fusion.","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/1906.03516v3","title":"DiCENet: Dimension-wise Convolutions for Efficient Networks","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":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/dicenet-dimension-wise-convolutions-for","title":"DiCENet: Dimension-wise Convolutions for Efficient Networks","date":"2019-06-08","arxiv_id":"1906.03516","n_code_links":2,"syntology":{"ran":0,"of":1,"unverified":1,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/architecture-search","name":"Neural Architecture Search","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/real-time-object-detection","name":"Real-Time Object Detection","papers":1},{"task":"/task/real-time-semantic-segmentation","name":"Real-Time Semantic Segmentation","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":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/dimfuse"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}