发表机构
Huawei Technologies Co., Ltd.（华为技术有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对个人风格视频精彩片段检测的传统方法泛化性差的问题，提出TRINITY多视角基准测试集与对应多分支架构，在两个数据集上显著优于现有方法，验证了多视角建模的有效性。
AI 中文摘要
传统视频精彩片段检测依赖于以事件为中心的狭隘显著性定义，通常无法泛化到无约束的个人视频，这类视频的精彩片段具有异质性且依赖视角。为解决该问题，我们引入TRINITY，这是一种多视角基准测试集，它在统一时间框架内将精彩片段显著性分解为三个互补维度：事件、情感和自然。利用这种多视角视角，我们提出了一种共享主干多分支架构，旨在通过视角特定专家进行并行多视角预测。综合实验表明，我们的方法显著优于现有最佳基线，在Mr. HiSum数据集上实现了+7.15/+3.62 mAP（rho=15%/50%）的提升，在YouTube Highlights数据集上实现了+10.82 mAP的提升。这些结果验证了多视角建模为视频显著性提供了更鲁棒和全面的表述，尤其适用于复杂的现实场景。该基准测试集和相关代码将在录用后发布，基准测试集可通过此https URL获取，代码可通过此https URL获取。
英文摘要
Traditional video highlight detection relies on a narrow, event-centric definition of saliency, which often fails to generalize to unconstrained personal videos where highlights are heterogeneous and perspective-dependent. To address this, we introduce TRINITY, a multi-perspective benchmark that decomposes highlight saliency into three complementary dimensions, Event, Emotion, and Nature, within a unified temporal framework. Leveraging this multi-faceted view, we propose a shared-backbone multi-branch architecture designed for parallel multi-perspective prediction via view-specific experts. Comprehensive experiments demonstrate that our method significantly outperforms state-of-the-art baselines, achieving gains of +7.15/+3.62 mAP (rho=15%/50%) on Mr. HiSum and +10.82 mAP on YouTube Highlights. These results validate that multi-perspective modeling provides a more robust and comprehensive formulation of video saliency, especially for complex real-world scenarios. The benchmark and relevant codes will be released upon acceptance. The benchmark is available at https://huggingface.co/datasets/vanilladucky/TRINITY and the code is available at https://github.com/vanilladucky/TRINITY.
Comments32 pages, 9 figures. Accepted to ECCV 2026
