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

arXiv:2609.26056 · cs.CV, cs.AI · Submitted 2026-08-08 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-08

Updated: 2026-08-08

Comments: AAAI 25 - Towards Knowledgeable Foundational Models workshop

License: http://creativecommons.org/licenses/by/4.0/

The gist: Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare against professional standards, making their

Terminology

Accuracy — 12× in this paper · explained in 1 episode
Alignment — 12× in this paper · explained in 1 episode
Embeddings — 7× in this paper · explained in 2 episodes
Vision-language models — 7× in this paper · explained in 2 episodes
Benchmark — 6× in this paper · explained in 2 episodes
Contrastive Learning — 4× in this paper · explained in 5 episodes
Benchmarking — 3× in this paper · explained in 4 episodes
Clustering — 3× in this paper · explained in 2 episodes
In-Context Learning — 3× in this paper · explained in 4 episodes
RAG — 3× in this paper · explained in 3 episodes
Vision-Language Models (VLMs) — 3× in this paper · explained in 6 episodes
Grounding — 2× in this paper · explained in 3 episodes
Retrieval-Augmented Generation — 2× in this paper
Action Space — 1× in this paper · explained in 1 episode
Adaptability — 1× in this paper · explained in 1 episode
Few-Shot Learning — 1× in this paper · explained in 4 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
Semantic Similarity — 1× in this paper · explained in 2 episodes
Structured Prompting — 1× in this paper · explained in 2 episodes
Transformer — 1× in this paper · explained in 2 episodes

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.

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

Terminology

Abstract

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