对比七种深度学习模型，发现多数论文宣称的高准确率在实际复现中大幅下降。
Modern Deep Learning Approaches for Cricket Shot Classification: A Comprehensive Baseline Study
- 统一基准测试七种模型，涵盖CNN-LSTM、视觉Transformer等不同范式。
- 复现结果显示最高仅92.25%准确率，远低于文献声称的99.2%。
- 提供可复现代码与MLOps实践，强调标准化评估的重要性。
板球击球动作分类从视频序列中识别击球类型仍是体育视频分析中的挑战，需有效建模空间与时间特征。本文首次系统性地比较了七种深度学习方法在四个不同研究范式下的表现。我们在统一基准上实现了并评估了传统CNN-LSTM架构、注意力模型、视觉变换器、迁移学习方法及现代EfficientNet-GRU组合。关键发现是学术文献中宣称的性能与实际实现结果之间存在显著差距：此前报告的96%（Balaji LRCN）、99.2%（IJERCSE）和93%（Sensors）准确率，在标准化复现后分别降至46.0%、55.6%和57.7%。我们提出的现代最优方法——结合EfficientNet-B0与基于GRU的时间建模，达到92.25%准确率，表明通过现代架构与系统优化可实现显著提升。所有实现均采用PyTorch Lightning遵循现代MLOps实践，构建了一个可复现的研究平台，凸显了标准化评估协议在体育视频分析研究中的关键作用。
原文摘要 · Abstract (English)
Cricket shot classification from video sequences remains a challenging problem in sports video analysis, requiring effective modeling of both spatial and temporal features. This paper presents the first comprehensive baseline study comparing seven different deep learning approaches across four distinct research paradigms for cricket shot classification. We implement and systematically evaluate traditional CNN-LSTM architectures, attention-based models, vision transformers, transfer learning approaches, and modern EfficientNet-GRU combinations on a unified benchmark. A critical finding of our study is the significant performance gap between claims in academic literature and practical implementation results. While previous papers reported accuracies of 96\% (Balaji LRCN), 99.2\% (IJERCSE), and 93\% (Sensors), our standardized re-implementations achieve 46.0\%, 55.6\%, and 57.7\% respectively. Our modern SOTA approach, combining EfficientNet-B0 with a GRU-based temporal model, achieves 92.25\% accuracy, demonstrating that substantial improvements are possible with modern architectures and systematic optimization. All implementations follow modern MLOps practices with PyTorch Lightning, providing a reproducible research platform that exposes the critical importance of standardized evaluation protocols in sports video analysis research.
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