# Spatio-temporal Video Re-localization by Warp LSTM

arXiv: https://arxiv.org/abs/1905.03922 · HTML: https://mlsanity.com/paper/1905.03922/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 8 of 8)

- [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://mlsanity.com/paper/2201.08071/) (2022)
- [INT: Towards Infinite-frames 3D Detection with An Efficient Framework](https://mlsanity.com/paper/2209.15215/) (2022)
- [Learning Collaborative Knowledge with Multimodal Representation for Polyp Re-Identification](https://mlsanity.com/paper/2408.05914/) (2024)
- [Few-Shot Transformation of Common Actions into Time and Space](https://mlsanity.com/paper/2104.02439/) (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)
- [Learning Discriminative Visual-Text Representation for Polyp Re-Identification](https://mlsanity.com/paper/2307.10625/) (2023)
- [GPF-Net: Gated Progressive Fusion Learning for Polyp Re-Identification](https://mlsanity.com/paper/2512.21476/) (2025)

## Abstract (arXiv)

The need for efficiently finding the video content a user wants is increasing because of the erupting of user-generated videos on the Web. Existing keyword-based or content-based video retrieval methods usually determine what occurs in a video but not when and where. In this paper, we make an answer to the question of when and where by formulating a new task, namely spatio-temporal video re-localization. Specifically, given a query video and a reference video, spatio-temporal video re-localization aims to localize tubelets in the reference video such that the tubelets semantically correspond to the query. To accurately localize the desired tubelets in the reference video, we propose a novel warp LSTM network, which propagates the spatio-temporal information for a long period and thereby captures the corresponding long-term dependencies. Another issue for spatio-temporal video re-localization is the lack of properly labeled video datasets. Therefore, we reorganize the videos in the AVA dataset to form a new dataset for spatio-temporal video re-localization research. Extensive experimental results show that the proposed model achieves superior performances over the designed baselines on the spatio-temporal video re-localization task.