用少量标注数据+大量无标注数据，提升游戏视觉缺陷检测效果
A Hybrid Co-Finetuning Approach for Visual Bug Detection in Video Games
- 结合目标游戏和跨游戏的有标签数据，融合无标签数据增强特征学习
- 仅用目标游戏一半标注数据，性能仍优于传统方法
- 适合标注数据稀缺的游戏开发场景，提升检测效率
游戏视觉缺陷的人工检测耗时且成本高，依赖大量标注数据的监督模型难以应用。为此，本文提出一种混合协同微调（CFT）方法，利用目标游戏和多种跨域游戏的有标签数据，并引入无标签数据以增强特征表示学习。该策略充分挖掘所有可用数据，显著降低对目标游戏特定标注数据的依赖。实验表明，所提框架在多个游戏环境中的视觉缺陷检测任务中表现更优，具备更强的可扩展性与适应性；即使仅使用目标游戏50%的标注数据，仍保持竞争力。
原文摘要 · Abstract (English)
Manual identification of visual bugs in video games is a resource-intensive and costly process, often demanding specialized domain knowledge. While supervised visual bug detection models offer a promising solution, their reliance on extensive labeled datasets presents a significant challenge due to the infrequent occurrence of such bugs. To overcome this limitation, we propose a hybrid Co-FineTuning (CFT) method that effectively integrates both labeled and unlabeled data. Our approach leverages labeled samples from the target game and diverse co-domain games, additionally incorporating unlabeled data to enhance feature representation learning. This strategy maximizes the utility of all available data, substantially reducing the dependency on labeled examples from the specific target game. The developed framework demonstrates enhanced scalability and adaptability, facilitating efficient visual bug detection across various game titles. Our experimental results show the robustness of the proposed method for game visual bug detection, exhibiting superior performance compared to conventional baselines across multiple gaming environments. Furthermore, CFT maintains competitive performance even when trained with only 50% of the labeled data from the target game.
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