Weak Supervision for Label Efficient Visual Bug Detection

Understand

As video games evolve into expansive, detailed worlds, visual quality becomes essential, yet increasingly challenging.

• Traditional testing methods, limited by resources, face difficulties in addressing the plethora of potential bugs.
• Machine learning offers scalable solutions; however, heavy reliance on large labeled datasets remains a constraint.
• Addressing this challenge, we propose a novel method, utilizing unlabeled gameplay and domain-specific augmentations to generate datasets & self-supervised objectives used during pre-training or multi-task settings for downstream visual bug detection.