# Localizing the Common Action Among a Few Videos

arXiv: https://arxiv.org/abs/2008.05826 · HTML: https://mlsanity.com/paper/2008.05826/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Few-Shot Temporal Action Localization with Query Adaptive Transformer](https://mlsanity.com/paper/2110.10552/) (2021)
- [Multi-Modal Few-Shot Temporal Action Detection](https://mlsanity.com/paper/2211.14905/) (2022)
- [PLOT-TAL: Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization](https://mlsanity.com/paper/2403.18915/) (2024)
- [Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain in Horses](https://mlsanity.com/paper/2105.10313/) (2021)
- [Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models](https://mlsanity.com/paper/2308.13082/) (2023)
- [Chain-of-Evidence Multimodal Reasoning for Few-shot Temporal Action Localization](https://mlsanity.com/paper/2504.13460/) (2025)
- [Few-Shot Transformation of Common Actions into Time and Space](https://mlsanity.com/paper/2104.02439/) (2021)
- [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)
- [Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection](https://mlsanity.com/paper/2311.00278/) (2023)
- [FMI-TAL: Few-shot Multiple Instances Temporal Action Localization by Probability Distribution Learning and Interval Cluster Refinement](https://mlsanity.com/paper/2408.13765/) (2024)

## Abstract (arXiv)

This paper strives to localize the temporal extent of an action in a long untrimmed video. Where existing work leverages many examples with their start, their ending, and/or the class of the action during training time, we propose few-shot common action localization. The start and end of an action in a long untrimmed video is determined based on just a hand-full of trimmed video examples containing the same action, without knowing their common class label. To address this task, we introduce a new 3D convolutional network architecture able to align representations from the support videos with the relevant query video segments. The network contains: (textiti) a mutual enhancement module to simultaneously complement the representation of the few trimmed support videos and the untrimmed query video; (textitii) a progressive alignment module that iteratively fuses the support videos into the query branch; and (textitiii) a pairwise matching module to weigh the importance of different support videos. Evaluation of few-shot common action localization in untrimmed videos containing a single or multiple action instances demonstrates the effectiveness and general applicability of our proposal.