Efficient Detection Transformer Based on Learnable Query Queuing and Encoder Self-Distillation

Detection Transformers (DETRs) have achieved notable performance and outperformed traditional deep neural network models in object detection. However, the self-attention of encoder tokens incurs significant computational overhead. To improve computational efficiency and maintain detection precision simultaneously, we propose an efficient detection transformer model based on learnable query queuing and encoder self-distillation, QD-DETR. Specifically, we first design a query queuing mechanism to select object-relevant queries with the guidance of supervising signals and spatial features. Second, we construct the query representation variational information bottleneck module to optimize the query queuing via mutual information regularization. Lastly, we develop an encoder feature self-distillation method to compensate for the information loss by directly distilling the encoder output token sequences. We conducted experiments on multiple datasets and verified the accuracy and efficiency of QD-DETR in comparison with mainstream detection models. The experiment results demonstrate that QD-DETR reduces computational complexity by 46% and increases inference speed by 69% while maintaining high accuracy in complex object detection scenarios.