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

TL;DR

This paper introduces a Boosted Model Ensembling approach that combines multiple variants of a baseline model to improve ball action spotting in videos, achieving second place in the CVPR'23 SoccerNet Challenge.

Contribution

The paper presents a novel ensemble strategy, Boosted Model Ensembling, for enhancing video action detection performance by selecting and weighting model variants.

Findings

Achieved second place in CVPR'23 SoccerNet Challenge

Demonstrated improved performance through model ensembling

Provided an effective strategy for model selection and weighting

Abstract

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.