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The authors present a multimodal system that automatically extracts high‑energy video segments for dynamic covers by analyzing subtitles, audio, visual frames, and danmu, employing LLM prompt‑tuning, scene‑cut detection, and aesthetic scoring to reduce manual effort and boost click‑through rates.
This article presents Weibo's comprehensive multimedia content understanding pipeline, covering video summarization techniques, quality assessment models, OCR advancements, video embedding strategies, and the performance benefits of CV‑CUDA acceleration, while highlighting real‑world applications and engineering trade‑offs.
The article describes the CCL2022 Video Highlight Extraction Challenge, a competition at the 21st China Conference on Computational Linguistics organized by Baidu, inviting participants worldwide to generate timestamped concise summaries of video segments, with registration details, eligibility, task description, example inputs/outputs, and evaluation metrics based on timing accuracy and ROUGE-L.
The paper proposes a 3D‑CNN scoring system that ranks short, information‑dense video segments, selects the most exciting one, and converts it into a looping GIF cover, replacing static thumbnails; trained on large video‑GIF datasets with pairwise ranking loss, it improves click‑through rates while reducing bad‑case generation.