# Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts

arXiv: https://arxiv.org/abs/2104.06779 · HTML: https://mlsanity.com/paper/2104.06779/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [R-C3D: Region Convolutional 3D Network for Temporal Activity Detection](https://mlsanity.com/paper/1703.07814/) (2017)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks](https://mlsanity.com/paper/2011.11479/) (2020)
- [A Context-Aware Loss Function for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1912.01326/) (2019)
- [Improved Soccer Action Spotting using both Audio and Video Streams](https://mlsanity.com/paper/2011.04258/) (2020)
- [Soccer on Your Tabletop](https://mlsanity.com/paper/1806.00890/) (2018)
- [Using Player's Body-Orientation to Model Pass Feasibility in Soccer](https://mlsanity.com/paper/2004.07209/) (2020)
- [RMS-Net: Regression and Masking for Soccer Event Spotting](https://mlsanity.com/paper/2102.07624/) (2021)
- [Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions](https://mlsanity.com/paper/2004.06172/) (2020)
- [Detecting the Moment of Completion: Temporal Models for Localising Action Completion](https://mlsanity.com/paper/1710.02310/) (2017)

## Built on by (most cited first, 30 of 30)

- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [Spotting Temporally Precise, Fine-Grained Events in Video](https://mlsanity.com/paper/2207.10213/) (2022)
- [MatchTime: Towards Automatic Soccer Game Commentary Generation](https://mlsanity.com/paper/2406.18530/) (2024)
- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection](https://mlsanity.com/paper/2106.14447/) (2021)
- [Temporally Precise Action Spotting in Soccer Videos Using Dense Detection Anchors](https://mlsanity.com/paper/2205.10450/) (2022)
- [ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton](https://mlsanity.com/paper/2112.01044/) (2021)
- [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)
- [Towards AI-Powered Video Assistant Referee System (VARS) for Association Football](https://mlsanity.com/paper/2407.12483/) (2024)
- [Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives](https://mlsanity.com/paper/2309.12067/) (2023)
- [Foul prediction with estimated poses from soccer broadcast video](https://mlsanity.com/paper/2402.09650/) (2024)
- [Deep learning for action spotting in association football videos](https://mlsanity.com/paper/2410.01304/) (2024)
- [Estimation of control area in badminton doubles with pose information from top and back view drone videos](https://mlsanity.com/paper/2305.04247/) (2023)
- [A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games](https://mlsanity.com/paper/2108.11149/) (2021)
- [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)
- [SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection](https://mlsanity.com/paper/2501.09281/) (2025)
- [Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion](https://mlsanity.com/paper/2403.12406/) (2024)
- [Do We Need Large VLMs for Spotting Soccer Actions?](https://mlsanity.com/paper/2506.17144/) (2025)
- [Event Detection in Football using Graph Convolutional Networks](https://mlsanity.com/paper/2301.10052/) (2023)
- [TrajSV: A Trajectory-based Model for Sports Video Representations and Applications](https://mlsanity.com/paper/2508.11569/) (2025)
- [Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology](https://mlsanity.com/paper/2605.09152/) (2026)
- [BasketEvent: Understanding Who Did What and When in Basketball Videos](https://mlsanity.com/paper/2607.21267/) (2026)
- [Entity-Aware Sequence Transduction for Player-Centric Ball Action Spotting](https://mlsanity.com/paper/2608.01696/) (2026)
- [BMASH: Ball-Motion-Aware Soccer Header Spotting](https://mlsanity.com/paper/2609.39300/) (2026)

## Abstract (arXiv)

Toward the goal of automatic production for sports broadcasts, a paramount task consists in understanding the high-level semantic information of the game in play. For instance, recognizing and localizing the main actions of the game would allow producers to adapt and automatize the broadcast production, focusing on the important details of the game and maximizing the spectator engagement. In this paper, we focus our analysis on action spotting in soccer broadcast, which consists in temporally localizing the main actions in a soccer game. To that end, we propose a novel feature pooling method based on NetVLAD, dubbed NetVLAD++, that embeds temporally-aware knowledge. Different from previous pooling methods that consider the temporal context as a single set to pool from, we split the context before and after an action occurs. We argue that considering the contextual information around the action spot as a single entity leads to a sub-optimal learning for the pooling module. With NetVLAD++, we disentangle the context from the past and future frames and learn specific vocabularies of semantics for each subsets, avoiding to blend and blur such vocabulary in time. Injecting such prior knowledge creates more informative pooling modules and more discriminative pooled features, leading into a better understanding of the actions. We train and evaluate our methodology on the recent large-scale dataset SoccerNet-v2, reaching 53.4% Average-mAP for action spotting, a +12.7% improvement w.r.t the current state-of-the-art.