{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/no-attention-is-needed-grouped-spatial","title":"A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift","arxiv_id":"2206.10810","date":"2022-06-22","proceeding":"CVPR 2023 1","authors":["Dasong Li","Xiaoyu Shi","Yi Zhang","Ka Chun Cheung","Simon See","Xiaogang Wang","Hongwei Qin","Hongsheng Li"],"abstract":"Video restoration, which aims to restore clear frames from degraded videos, has numerous important applications. The key to video restoration depends on utilizing inter-frame information. However, existing deep learning methods often rely on complicated network architectures, such as optical flow estimation, deformable convolution, and cross-frame self-attention layers, resulting in high computational costs. In this study, we propose a simple yet effective framework for video restoration. Our approach is based on grouped spatial-temporal shift, which is a lightweight and straightforward technique that can implicitly capture inter-frame correspondences for multi-frame aggregation. By introducing grouped spatial shift, we attain expansive effective receptive fields. Combined with basic 2D convolution, this simple framework can effectively aggregate inter-frame information. Extensive experiments demonstrate that our framework outperforms the previous state-of-the-art method, while using less than a quarter of its computational cost, on both video deblurring and video denoising tasks. These results indicate the potential for our approach to significantly reduce computational overhead while maintaining high-quality results. Code is avaliable at https://github.com/dasongli1/Shift-Net.","url_abs":"https://arxiv.org/abs/2206.10810v2","url_pdf":"https://arxiv.org/pdf/2206.10810v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"no-attention-is-needed-grouped-spatial","repo_url":"https://github.com/dasongli1/shift-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"video-deblurring","task_name":"Video Deblurring"},{"task_slug":"video-denoising","task_name":"Video Denoising"},{"task_slug":"video-restoration","task_name":"Video Restoration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/deblurring-on-dvd-1","task":"Deblurring","dataset":"DVD","model":"GShift-Net","rank_in_archive_order":3,"of":7,"metrics":{"PSNR":"34.69","SSIM":"0.969"},"uses_additional_data":false},{"leaderboard":"/sota/deblurring-on-gopro","task":"Deblurring","dataset":"GoPro","model":"GShift-Net","rank_in_archive_order":2,"of":56,"metrics":{"PSNR":"35.88","SSIM":"0.979"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.10810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.10810"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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