Scale-aware token-matching for transformer-based object detector

Owing to the advancements in deep learning, object detection has made significant progress in estimating the positions and classes of multiple objects within an image. However, detecting objects of various scales within a single image remains a challenging problem. In this study, we suggest a scale-aware token matching to predict the positions and classes of objects for transformer-based object detection. We train a model by matching detection tokens with ground truth considering its size, unlike the previous methods that performed matching without considering the scale during the training process. We divide one detection token set into multiple sets based on scale and match each token set differently with ground truth, thereby, training the model without additional computation costs. The experimental results demonstrate that scale information can be assigned to tokens. Scale-aware tokens can independently learn scale-specific information by using a novel loss function, which improves the detection performance on small objects.