Do We Need Large VLMs for Spotting Soccer Actions?
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915518559027200 |
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| author | Chakraborty, Ritabrata Chakraborty, Rajatsubhra Dasgupta, Avijit Chaurasia, Sandeep |
| author_facet | Chakraborty, Ritabrata Chakraborty, Rajatsubhra Dasgupta, Avijit Chaurasia, Sandeep |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Do We Need Large VLMs for Spotting Soccer Actions? Chakraborty, Ritabrata Chakraborty, Rajatsubhra Dasgupta, Avijit Chaurasia, Sandeep Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Do We Need Large VLMs for Spotting Soccer Actions? |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.17144 |