# RMS-Net: Regression and Masking for Soccer Event Spotting

arXiv: https://arxiv.org/abs/2102.07624 · HTML: https://mlsanity.com/paper/2102.07624/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [YouTube-8M: A Large-Scale Video Classification Benchmark](https://mlsanity.com/paper/1609.08675/) (2016)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [The THUMOS Challenge on Action Recognition for Videos "in the Wild"](https://mlsanity.com/paper/1604.06182/) (2016)
- [Graph Convolutional Networks for Temporal Action Localization](https://mlsanity.com/paper/1909.03252/) (2019)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [Spatiotemporal Residual Networks for Video Action Recognition](https://mlsanity.com/paper/1611.02155/) (2016)
- [Long-term Temporal Convolutions for Action Recognition](https://mlsanity.com/paper/1604.04494/) (2016)
- [A Context-Aware Loss Function for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1912.01326/) (2019)
- [A Multigrid Method for Efficiently Training Video Models](https://mlsanity.com/paper/1912.00998/) (2019)
- [Improved Soccer Action Spotting using both Audio and Video Streams](https://mlsanity.com/paper/2011.04258/) (2020)
- [Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions](https://mlsanity.com/paper/2004.06172/) (2020)
- [SoccerDB: A Large-Scale Database for Comprehensive Video Understanding](https://mlsanity.com/paper/1912.04465/) (2019)

## Built on by (most cited first, 19 of 19)

- [Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting](https://mlsanity.com/paper/2104.09333/) (2021)
- [Spotting Temporally Precise, Fine-Grained Events in Video](https://mlsanity.com/paper/2207.10213/) (2022)
- [Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts](https://mlsanity.com/paper/2104.06779/) (2021)
- [Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors](https://mlsanity.com/paper/2205.10450/) (2022)
- [COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers](https://mlsanity.com/paper/2309.01270/) (2023)
- [Action Spotting using Dense Detection Anchors Revisited: Submission to the SoccerNet Challenge 2022](https://mlsanity.com/paper/2206.07846/) (2022)
- [Towards Active Learning for Action Spotting in Association Football Videos](https://mlsanity.com/paper/2304.04220/) (2023)
- [A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification](https://mlsanity.com/paper/2211.12334/) (2022)
- [Action Anticipation from SoccerNet Football Video Broadcasts](https://mlsanity.com/paper/2504.12021/) (2025)
- [Unifying Global and Local Scene Entities Modelling for Precise Action Spotting](https://mlsanity.com/paper/2404.09951/) (2024)
- [Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives](https://mlsanity.com/paper/2309.12067/) (2023)
- [Deep learning for action spotting in association football videos](https://mlsanity.com/paper/2410.01304/) (2024)
- [Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance](https://mlsanity.com/paper/2503.00147/) (2025)
- [A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games](https://mlsanity.com/paper/2108.11149/) (2021)
- [Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation](https://mlsanity.com/paper/2202.05728/) (2022)
- [Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges](https://mlsanity.com/paper/2505.03991/) (2025)
- [OSL-ActionSpotting: A Unified Library for Action Spotting in Sports Videos](https://mlsanity.com/paper/2407.01265/) (2024)
- [Do We Need Large VLMs for Spotting Soccer Actions?](https://mlsanity.com/paper/2506.17144/) (2025)
- [TrajSV: A Trajectory-based Model for Sports Video Representations and Applications](https://mlsanity.com/paper/2508.11569/) (2025)

## Abstract (arXiv)

The recently proposed action spotting task consists in finding the exact timestamp in which an event occurs. This task fits particularly well for soccer videos, where events correspond to salient actions strictly defined by soccer rules (a goal occurs when the ball crosses the goal line). In this paper, we devise a lightweight and modular network for action spotting, which can simultaneously predict the event label and its temporal offset using the same underlying features. We enrich our model with two training strategies: the first one for data balancing and uniform sampling, the second for masking ambiguous frames and keeping the most discriminative visual cues. When tested on the SoccerNet dataset and using standard features, our full proposal exceeds the current state of the art by 3 Average-mAP points. Additionally, it reaches a gain of more than 10 Average-mAP points on the test set when fine-tuned in combination with a strong 2D backbone.