# Video Re-localization

arXiv: https://arxiv.org/abs/1808.01575 · HTML: https://mlsanity.com/paper/1808.01575/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2018.

## Built on by (most cited first, 17 of 17)

- [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://mlsanity.com/paper/2201.08071/) (2022)
- [DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval](https://mlsanity.com/paper/2106.13266/) (2021)
- [Few-Shot Temporal Action Localization with Query Adaptive Transformer](https://mlsanity.com/paper/2110.10552/) (2021)
- [Scaling up sign spotting through sign language dictionaries](https://mlsanity.com/paper/2205.04152/) (2022)
- [Self-Supervised Video Similarity Learning](https://mlsanity.com/paper/2304.03378/) (2023)
- [Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval](https://mlsanity.com/paper/2309.11091/) (2023)
- [Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval](https://mlsanity.com/paper/2407.15566/) (2024)
- [VADER: Video Alignment Differencing and Retrieval](https://mlsanity.com/paper/2303.13193/) (2023)
- [Self-supervised Video Retrieval Transformer Network](https://mlsanity.com/paper/2104.07993/) (2021)
- [A Fast Partial Video Copy Detection Using KNN and Global Feature Database](https://mlsanity.com/paper/2105.01713/) (2021)
- [Chain-of-Evidence Multimodal Reasoning for Few-shot Temporal Action Localization](https://mlsanity.com/paper/2504.13460/) (2025)
- [Aligning Moments in Time using Video Queries](https://mlsanity.com/paper/2508.15439/) (2025)
- [Few-Shot Action Localization without Knowing Boundaries](https://mlsanity.com/paper/2106.04150/) (2021)
- [Less than Few: Self-Shot Video Instance Segmentation](https://mlsanity.com/paper/2204.08874/) (2022)
- [Semantic Video Moments Retrieval at Scale: A New Task and a Baseline](https://mlsanity.com/paper/2210.08389/) (2022)
- [FMI-TAL: Few-shot Multiple Instances Temporal Action Localization by Probability Distribution Learning and Interval Cluster Refinement](https://mlsanity.com/paper/2408.13765/) (2024)
- [SignMatch: Matching Dictionary Signs to Continuous Sign Language Video](https://mlsanity.com/paper/2609.01886/) (2026)

## Abstract (arXiv)

Many methods have been developed to help people find the video contents they want efficiently. However, there are still some unsolved problems in this area. For example, given a query video and a reference video, how to accurately localize a segment in the reference video such that the segment semantically corresponds to the query video? We define a distinctively new task, namely video re-localization, to address this scenario. Video re-localization is an important emerging technology implicating many applications, such as fast seeking in videos, video copy detection, video surveillance, etc. Meanwhile, it is also a challenging research task because the visual appearance of a semantic concept in videos can have large variations. The first hurdle to clear for the video re-localization task is the lack of existing datasets. It is labor expensive to collect pairs of videos with semantic coherence or correspondence and label the corresponding segments. We first exploit and reorganize the videos in ActivityNet to form a new dataset for video re-localization research, which consists of about 10,000 videos of diverse visual appearances associated with localized boundary information. Subsequently, we propose an innovative cross gated bilinear matching model such that every time-step in the reference video is matched against the attentively weighted query video. Consequently, the prediction of the starting and ending time is formulated as a classification problem based on the matching results. Extensive experimental results show that the proposed method outperforms the competing methods. Our code is available at: https://github.com/fengyang0317/videoreloc.