mlsanityData to 14 Oct 2026

A Boosted Model Ensembling Approach to Ball Action Spotting in Videos: The Runner-Up Solution to CVPR'23 SoccerNet Challenge

arXiv 2306.05772 ↗First posted 9 Jun 2023cs.CVcited by 4

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Abstract (arXiv)

This technical report presents our solution to Ball Action Spotting in videos. Our method reached second place in the CVPR'23 SoccerNet Challenge. Details of this challenge can be found at https://www.soccer-net.org/tasks/ball-action-spotting. Our approach is developed based on a baseline model termed E2E-Spot, which was provided by the organizer of this competition. We first generated several variants of the E2E-Spot model, resulting in a candidate model set. We then proposed a strategy for selecting appropriate model members from this set and assigning an appropriate weight to each model. The aim of this strategy is to boost the performance of the resulting model ensemble. Therefore, we call our approach Boosted Model Ensembling (BME). Our code is available at https://github.com/ZJLAB-AMMI/E2E-Spot-MBS.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 10 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

10 of 10 references.

References · 10

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  4. 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,209
  5. 2020Designing Network Design Spacescited by 960
  6. 2017Learning non-maximum suppressioncited by 113
  7. 2019Gate-Shift Networks for Video Action Recognitioncited by 76
  8. 2022Spotting Temporally Precise, Fine-Grained Events in Videocited by 40
  9. 2021Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detectioncited by 27
  10. 2022Stochastic Weight Averaging Revisitedcited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2024SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimapcited by 34
  2. 2023SoccerNet 2023 Challenges Resultscited by 20
  3. 2024Deep learning for action spotting in association football videoscited by 7
  4. 2026Entity-Aware Sequence Transduction for Player-Centric Ball Action Spottingcited by 0