使用注意力机制的3D CNN的MediaEval 2022运动任务的基线方法
Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms
摘要：
本文介绍了为MediaEval 2022基准测试的体育视频任务部分提出的基线方法。该任务提出了两个子任务：修剪视频中的笔划分类和未修剪视频中的笔划检测。此基线处理两个子任务。我们提出了两种类型的3D-CNN架构来解决这两个子任务。这两种3D-CNN都使用时空卷积和注意力机制。体系结构和训练过程是针对解决所解决的子任务而定制的。这种基线方法在网上公开共享，以帮助参与者进行调查，并最终减轻任务的某些方面，例如视频处理、培训方法、评估和提交程序。基线方法达到86.4%的准确率与我们的v2模型的分类子任务。对于检测子任务，使用我们的v1模型，基线达到0.131的mAP和0.515的IoU。
英文摘要：
This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2022 benchmark. This task proposes two subtasks: stroke classification from trimmed videos, and stroke detection from untrimmed videos. This baseline addresses both subtasks. We propose two types of 3D-CNN architectures to solve the two subtasks. Both 3D-CNNs use Spatio-temporal convolutions and attention mechanisms. The architectures and the training process are tailored to solve the addressed subtask. This baseline method is shared publicly online to help the participants in their investigation and alleviate eventually some aspects of the task such as video processing, training method, evaluation and submission routine. The baseline method reaches 86.4% of accuracy with our v2 model for the classification subtask. For the detection subtask, the baseline reaches a mAP of 0.131 and IoU of 0.515 with our v1 model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
