Abstract
The rapid expansion of video content across online platforms has accelerated the need for retrieval systems capable of understanding not only isolated visual moments but also the temporal structure of complex events. Existing approaches often fall short in modeling temporal dependencies across multiple events and in handling queries that reference unseen or rare visual concepts. To address these challenges, we introduce MADTempo, a video retrieval framework developed by our team, AIO_Trinh, that unifies temporal search with web-scale visual grounding. Our temporal search mechanism captures event-level continuity by aggregating similarity scores across sequential video segments, enabling coherent retrieval of multi-event queries. Complementarily, a Google Image Search-based fallback module expands query representations with external web imagery, effectively bridging gaps in pretrained visual embeddings and improving robustness against out-of-distribution (OOD) queries. Together, these components advance the temporal rea- soning and generalization capabilities of modern video retrieval systems, paving the way for more semantically aware and adaptive retrieval across large-scale video corpora.
Abstract (translated)
在线平台上视频内容的迅速扩张加速了对能够理解不仅孤立的视觉时刻，还能理解复杂事件时间结构的检索系统的需求。现有方法在建模多事件间的时间依赖性和处理引用未见过或罕见视觉概念的查询方面常常表现不足。为了解决这些挑战，我们团队AIO_Trinh开发了一种名为MADTempo的视频检索框架，该框架将时间搜索与大规模网络图像识别统一起来。我们的时序搜索机制通过聚合连续视频片段间的相似性评分来捕捉事件级别的连贯性，从而能够有效地检索多事件查询。此外，一个基于Google图片搜索的后备模块利用外部网络图像扩充查询表示，有效填补预训练视觉嵌入中的空白，并提高了对抗分布外（OOD）查询的能力。这些组件共同推进了现代视频检索系统的时序推理和泛化能力，在大规模视频语料库中实现了更具有语义意识和适应性的检索方式。
URL
https://arxiv.org/abs/2512.12929
