发表机构
Ontario Tech University; Universität Osnabrück（安大略理工大学; 奥斯纳布吕克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对游戏视频中多感知缺陷检测，提出ResNet-BiLSTM模型，在基准上F1达85.78%，并引入含77,969片段的新数据集，验证了时间建模的有效性。
AI 中文摘要
传统上，视频游戏中自动化缺陷检测的方法，如人工测试，虽有助于提升质量保证，但成本高昂且耗时。在现实场景中，现有工具难以在同一视频帧中检测多种感知缺陷，这给自动化缺陷检测工具带来了挑战。我们提出了一种用于多标签感知缺陷检测的深度学习模型，并将其与视频分类模型（如膨胀3D卷积网络和3D残差网络）进行比较。我们提出的模型ResNet-BiLSTM在基准数据集上取得了85.78%的F1分数。结果表明，时间依赖建模对于基于视频的准确缺陷检测是有益的。我们相信，这项针对游戏视频的多标签感知缺陷检测工作将有助于节省视频游戏中人工测试所消耗的资源。此外，我们在此工作中引入了一个包含多标签感知缺陷的新数据集。该数据集包含77,969个视频片段，涵盖不同游戏类型，约120万帧，其中包含同一视频帧中5类缺陷的组合。
英文摘要
Traditional approaches for automated bug detection in video games, such as manual testing, can be beneficial for the improvement of quality assurance, but they can be expensive and time-consuming. The scarce number of tools available to detect multiple perceptual bugs in the same video frame introduces detection challenges for automated bug detection tools in real-world scenarios. We propose a deep learning model for multi-label perceptual bug detection and compare it against video classification models such as Inflated 3D ConvNet and 3D ResNet. Our proposed model, ResNet-BiLSTM, achieved an F1 score of 85.78% on the benchmark dataset. Our results demonstrated that temporal dependency modelling is beneficial for accurate video-based bug detection. We believe this work with multi-label perceptual bug detection on gameplay videos will help save resources spent on manual testing workloads in video games. Furthermore, we introduce a new dataset with multi-label perceptual bugs in this work. The dataset contains 77,969 video clips across different genres of games with approximately 1.2 million frames, containing combinations from 5 classes of bugs in the same video frame.
