Do We Need Large VLMs for Spotting Soccer Actions?

• URL: http://arxiv.org/abs/2506.17144v2
• Date: Sat, 27 Sep 2025 15:21:30 GMT
• Title: Do We Need Large VLMs for Spotting Soccer Actions?
• Authors: Ritabrata Chakraborty, Rajatsubhra Chakraborty, Avijit Dasgupta, Sandeep Chaurasia,
• Abstract summary: We propose a shift from this video-centric approach to a text-based task, making it lightweight and scalable.<n>We posit that expert commentary, which provides rich descriptions and contextual cues contains sufficient information to reliably spot key actions in a match.<n>Our experiments show that this language-centric approach performs effectively in detecting critical match events coming close to state-of-the-art video-based spotters.
• Score: 4.175749804472612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional video-based tasks like soccer action spotting rely heavily on visual inputs, often requiring complex and computationally expensive models to process dense video data. We propose a shift from this video-centric approach to a text-based task, making it lightweight and scalable by utilizing Large Language Models (LLMs) instead of Vision-Language Models (VLMs). We posit that expert commentary, which provides rich descriptions and contextual cues contains sufficient information to reliably spot key actions in a match. To demonstrate this, we employ a system of three LLMs acting as judges specializing in outcome, excitement, and tactics for spotting actions in soccer matches. Our experiments show that this language-centric approach performs effectively in detecting critical match events coming close to state-of-the-art video-based spotters while using zero video processing compute and similar amount of time to process the entire match.

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