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Kuaishou partners with Dolby Labs to bring full‑chain Dolby Vision to its short‑video platform, detailing the technology behind HDR, dynamic metadata, and a brightness‑adjustment solution that ensures seamless playback and optimal visual experience for user‑generated content across devices.
MD‑VQA is a no‑reference video quality assessment model that combines semantic cues from EfficientNetV2, handcrafted distortion metrics, and motion information from ResNet3D‑18 to predict absolute quality of user‑generated live videos, trained on the large TaoLive dataset and achieving state‑of‑the‑art SRCC and PLCC results that are already deployed for real‑time monitoring on Taobao’s streaming platform.
Professor Zhai’s REDtech talk explained how the human visual system underlies full‑, reduced‑ and no‑reference media quality metrics, introduced a free‑energy‑based perception model and pseudo‑reference technique for accurate no‑reference UGC video assessment, and discussed audio‑visual integration, opinion‑score distributions, and EEG‑based perceptual loss challenges.
This article presents a comprehensive overview of Taobao's user‑generated content (UGC) sentiment analysis pipeline, covering background, task definition, challenges, model architecture—including RoBERTa‑based extraction, sentiment‑knowledge pre‑training, and graph augmentation—personalized impression ranking, business impact cases, and future research directions.
This article presents a comprehensive overview of Taobao's user‑generated content sentiment analysis pipeline, covering task definition, challenges, model architecture with RoBERTa‑based extraction, sentiment‑knowledge pre‑training, graph augmentation, personalized ranking, business impact metrics, and future research directions.
The article explains how the metaverse differs from traditional online games by enabling user‑generated content, unique digital assets secured by NFTs and blockchain, and a decentralized economy built on cloud, 5G, and VR technologies, illustrating its impact on the industry.
Chaplin Project replaces costly image‑to‑video synthesis on Xianyu by using centrally managed, OpenGL‑shader templates that combine product images with dynamic effects at playback, supported by a visual editor, resource platform, recommendation engine, and multi‑threaded client architecture, delivering richer browsing while conserving resources.
This article details how JD.com’s engineering team designed, implemented, and optimized a user‑generated‑content platform that went from concept to production in 60 days, covering project background, system architecture, data storage with JimDB, messaging decoupling, asynchronous I/O, performance testing, and future improvements.