# FMI-TAL: Few-shot Multiple Instances Temporal Action Localization by Probability Distribution Learning and Interval Cluster Refinement

arXiv: https://arxiv.org/abs/2408.13765 · HTML: https://mlsanity.com/paper/2408.13765/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 23 of 23)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [Temporal Action Detection with Structured Segment Networks](https://mlsanity.com/paper/1704.06228/) (2017)
- [Graph Convolutional Networks for Temporal Action Localization](https://mlsanity.com/paper/1909.03252/) (2019)
- [Rethinking the Faster R-CNN Architecture for Temporal Action Localization](https://mlsanity.com/paper/1804.07667/) (2018)
- [R-C3D: Region Convolutional 3D Network for Temporal Activity Detection](https://mlsanity.com/paper/1703.07814/) (2017)
- [End-to-end Temporal Action Detection with Transformer](https://mlsanity.com/paper/2106.10271/) (2021)
- [Bounding Box Regression with Uncertainty for Accurate Object Detection](https://mlsanity.com/paper/1809.08545/) (2018)
- [Relaxed Transformer Decoders for Direct Action Proposal Generation](https://mlsanity.com/paper/2102.01894/) (2021)
- [Multi-granularity Generator for Temporal Action Proposal](https://mlsanity.com/paper/1811.11524/) (2018)
- [CTAP: Complementary Temporal Action Proposal Generation](https://mlsanity.com/paper/1807.04821/) (2018)
- [Temporal Context Network for Activity Localization in Videos](https://mlsanity.com/paper/1708.02349/) (2017)
- [Graph Convolutional Module for Temporal Action Localization in Videos](https://mlsanity.com/paper/2112.00302/) (2021)
- [Proposal Relation Network for Temporal Action Detection](https://mlsanity.com/paper/2106.11812/) (2021)
- [Video Re-localization](https://mlsanity.com/paper/1808.01575/) (2018)
- [Temporal Action Proposal Generation with Background Constraint](https://mlsanity.com/paper/2112.07984/) (2021)
- [Few-Shot Temporal Action Localization with Query Adaptive Transformer](https://mlsanity.com/paper/2110.10552/) (2021)
- [Localizing the Common Action Among a Few Videos](https://mlsanity.com/paper/2008.05826/) (2020)
- [Two-Stream Region Convolutional 3D Network for Temporal Activity Detection](https://mlsanity.com/paper/1906.02182/) (2019)
- [Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/2206.10095/) (2022)

## Abstract (arXiv)

The present few-shot temporal action localization model can't handle the situation where videos contain multiple action instances. So the purpose of this paper is to achieve manifold action instances localization in a lengthy untrimmed query video using limited trimmed support videos. To address this challenging problem effectively, we proposed a novel solution involving a spatial-channel relation transformer with probability learning and cluster refinement. This method can accurately identify the start and end boundaries of actions in the query video, utilizing only a limited number of labeled videos. Our proposed method is adept at capturing both temporal and spatial contexts to effectively classify and precisely locate actions in videos, enabling a more comprehensive utilization of these crucial details. The selective cosine penalization algorithm is designed to suppress temporal boundaries that do not include action scene switches. The probability learning combined with the label generation algorithm alleviates the problem of action duration diversity and enhances the model's ability to handle fuzzy action boundaries. The interval cluster can help us get the final results with multiple instances situations in few-shot temporal action localization. Our model achieves competitive performance through meticulous experimentation utilizing the benchmark datasets ActivityNet1.3 and THUMOS14. Our code is readily available at https://github.com/ycwfs/FMI-TAL.