CricRAG: Retrieval Augmented Vision-Language Models for Personalized Cricket Coaching

Abstract:Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare against professional standards, making their feedback impractical for developing players. We present CricRAG, a retrieval-augmented framework that aligns VLMs with skill-appropriate benchmarks for personalized cricket coaching. Our key insight is that by retrieving similar-but-better techniques as reference points, we can guide VLMs to provide developmentally appropriate feedback that mirrors human coaching practices. We contribute: (1) a labelled dataset of 288 cricket technique videos spanning multiple skill levels, (2) an efficient motion retrieval pipeline using contrastive learning that achieves 78% top-3 retrieval accuracy, (3) a frame sampling technique that reduces inference costs, and (4) a retrieval-augmented approach that significantly improves feedback alignment with coaching principles, achieving up to 94% agreement with professional assessments compared to 67% without retrieval context.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2609.26056 [cs.CV]

(or arXiv:2609.26056v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.26056 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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