Multi-route training with instructive self-attention tokens and a route-aware mixture-of-experts raises detection mAP by 2 to 4 points across several DETR baselines at no inference cost.
MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism
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abstract
Based on analyzing the character of cascaded decoder architecture commonly adopted in existing DETR-like models, this paper proposes a new decoder architecture. The cascaded decoder architecture constrains object queries to update in the cascaded direction, only enabling object queries to learn relatively-limited information from image features. However, the challenges for object detection in natural scenes (e.g., extremely-small, heavily-occluded, and confusingly mixed with the background) require an object detection model to fully utilize image features, which motivates us to propose a new decoder architecture with the parallel Multi-time Inquiries (MI) mechanism. MI enables object queries to learn more comprehensive information, and our MI based model, MI-DETR, outperforms all existing DETR-like models on COCO benchmark under different backbones and training epochs, achieving +2.3 AP and +0.6 AP improvements compared to the most representative model DINO and SOTA model Relation-DETR under ResNet-50 backbone. In addition, a series of diagnostic and visualization experiments demonstrate the effectiveness, rationality, and interpretability of MI.
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Mr. DETR++: Instructive Multi-Route Training for Detection Transformers with Mixture-of-Experts
Multi-route training with instructive self-attention tokens and a route-aware mixture-of-experts raises detection mAP by 2 to 4 points across several DETR baselines at no inference cost.