Study proposes ResNet-BiLSTM for multi-label perceptual bug detection from gameplay footage
The study proposes a ResNet-BiLSTM model that performs multi-label perceptual bug detection on gameplay footage, achieving an F1 score of 85.78% on a benchmark dataset, and compares it with video classification models including Inflated 3D ConvNet and 3D ResNet.
Why it matters: The paper reports an F1 of 85.78% for ResNet-BiLSTM and compares it with video classification models such as Inflated 3D ConvNet on the same task.