{"url":"/method/ifblock","slug":"ifblock","name":"IFBlock","full_name":"IFBlock","full_name_withheld":false,"description_markdown":"**IFBlock** is a video model block used in the [IFNet](https://paperswithcode.com/method/ifnet) architecture for video frame interpolation. IFBlocks do not contain expensive operators like cost volume or forward warping and use 3 × 3 convolution and deconvolution as building blocks. Each IFBlock has a feed-forward structure consisting of several convolutional layers and an upsampling operator. Except for the layer that outputs the optical flow residuals and the fusion map, [PReLU](https://paperswithcode.com/method/prelu) activations are used.","description_state":"present","introduced_year":null,"introduced_by":{"title":"RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation","paper":"/paper/rife-real-time-intermediate-flow-estimation","first_author":"Zhewei Huang","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/rife-real-time-intermediate-flow-estimation"},"source":{"url":"https://arxiv.org/abs/2011.06294v11","title":"RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation","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":"Video Model Blocks","url":"/methods/category/video-model-blocks","pwc_aliases":[]}],"n_papers_tagged":6,"archive_num_papers":6,"papers_newest_first":[{"paper":null,"title":"l0-Regularized Sparse Coding-based Interpretable Network for Multi-Modal Image Fusion","date":"2024-11-07","arxiv_id":"2411.04519","n_code_links":0,"syntology":null},{"paper":null,"title":"IFNet: Deep Imaging and Focusing for Handheld SAR with Millimeter-wave Signals","date":"2024-05-03","arxiv_id":"2405.02023","n_code_links":0,"syntology":null},{"paper":"/paper/iterative-feedback-network-for-unsupervised","title":"Iterative Feedback Network for Unsupervised Point Cloud Registration","date":"2024-01-09","arxiv_id":"2401.04357","n_code_links":1,"syntology":null},{"paper":null,"title":"Point cloud completion via structured feature maps using a feedback network","date":"2022-02-17","arxiv_id":"2202.08583","n_code_links":0,"syntology":null},{"paper":"/paper/fastrife-optimization-of-real-time","title":"FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation","date":"2021-05-27","arxiv_id":"2105.13482","n_code_links":1,"syntology":null},{"paper":"/paper/rife-real-time-intermediate-flow-estimation","title":"RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation","date":"2020-11-12","arxiv_id":"2011.06294","n_code_links":13,"syntology":{"ran":19,"of":26,"unverified":7,"pointer_only":16}}],"papers_shown":6,"tasks":[{"task":"/task/video-frame-interpolation","name":"Video Frame Interpolation","papers":2},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/optical-flow-estimation","name":"Optical Flow Estimation","papers":1},{"task":"/task/point-cloud-completion","name":"Point Cloud Completion","papers":1},{"task":"/task/point-cloud-registration","name":"Point Cloud Registration","papers":1},{"task":"/task/ssim","name":"SSIM","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1},{"task":"/task/point-cloud-upsampling","name":"point cloud upsampling","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":1},{"year":"2022","papers":1},{"year":"2024","papers":3}],"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/ifblock"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}