针对工业视频搜索中的低质视频，提出多分支协同网络精准识别四类质量问题。
Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search
- 设计四个分支分别处理视觉、文本、语义等四类低质问题。
- 加权融合与注意力机制使评分更稳定，线上排名提升显著。
- 对AI生成视频的低质识别准确率明显优于基线模型。
视频质量评估（VQA）对大规模视频检索系统至关重要，旨在识别质量问题以优先展示高质量视频。在工业场景中，低质视频主要表现为四类：视觉问题（如马赛克、黑框）、文本问题（标题和OCR内容错误）、语义问题（帧间不连贯、图文不符），尤其在人工智能生成视频中尤为突出。尽管这些现象普遍存在，学术研究却长期忽视。为此，我们提出专为工业视频检索设计的多分支协同网络（MBCN），包含四个分支，分别对应上述四类问题。各分支独立评分后，通过加权聚合与挤压-激励机制动态整合结果，结合点对点和成对优化目标确保评分稳定性与合理性。在世界级视频搜索引擎上进行的离线与在线实验表明，MBCN能有效识别各类质量缺陷，显著提升检索排序性能。详细分析验证了所有四个分支的正向贡献，且对低质AI生成视频的识别准确率远超基线模型。
原文摘要 · Abstract (English)
Video Quality Assessment (VQA) is vital for large-scale video retrieval systems, aimed at identifying quality issues to prioritize high-quality videos. In industrial systems, low-quality video characteristics fall into four categories: visual-related issues like mosaics and black boxes, textual issues from video titles and OCR content, and semantic issues like frame incoherence and frame-text mismatch from AI-generated videos. Despite their prevalence in industrial settings, these low-quality videos have been largely overlooked in academic research, posing a challenge for accurate identification. To address this, we introduce the Multi-Branch Collaborative Network (MBCN) tailored for industrial video retrieval systems. MBCN features four branches, each designed to tackle one of the aforementioned quality issues. After each branch independently scores videos, we aggregate these scores using a weighted approach and a squeeze-and-excitation mechanism to dynamically address quality issues across different scenarios. We implement point-wise and pair-wise optimization objectives to ensure score stability and reasonableness. Extensive offline and online experiments on a world-level video search engine demonstrate MBCN's effectiveness in identifying video quality issues, significantly enhancing the retrieval system's ranking performance. Detailed experimental analyses confirm the positive contribution of all four evaluation branches. Furthermore, MBCN significantly improves recognition accuracy for low-quality AI-generated videos compared to the baseline.
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