{"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/zoom-vqa-patches-frames-and-clips-integration","title":"Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment","arxiv_id":"2304.06440","date":"2023-04-13","proceeding":null,"authors":["Kai Zhao","Kun Yuan","Ming Sun","Xing Wen"],"abstract":"Video quality assessment (VQA) aims to simulate the human perception of video quality, which is influenced by factors ranging from low-level color and texture details to high-level semantic content. To effectively model these complicated quality-related factors, in this paper, we decompose video into three levels (\\ie, patch level, frame level, and clip level), and propose a novel Zoom-VQA architecture to perceive spatio-temporal features at different levels. It integrates three components: patch attention module, frame pyramid alignment, and clip ensemble strategy, respectively for capturing region-of-interest in the spatial dimension, multi-level information at different feature levels, and distortions distributed over the temporal dimension. Owing to the comprehensive design, Zoom-VQA obtains state-of-the-art results on four VQA benchmarks and achieves 2nd place in the NTIRE 2023 VQA challenge. Notably, Zoom-VQA has outperformed the previous best results on two subsets of LSVQ, achieving 0.8860 (+1.0%) and 0.7985 (+1.9%) of SRCC on the respective subsets. Adequate ablation studies further verify the effectiveness of each component. Codes and models are released in https://github.com/k-zha14/Zoom-VQA.","url_abs":"https://arxiv.org/abs/2304.06440v1","url_pdf":"https://arxiv.org/pdf/2304.06440v1.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":"zoom-vqa-patches-frames-and-clips-integration","repo_url":"https://github.com/k-zha14/zoom-vqa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"video-quality-assessment","task_name":"Video Quality Assessment"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[{"method_slug":"clip","method_name":"CLIP"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.06440","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}