Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment

Qiang Chen, Xiaokang Chen, Jian Wang, Shan Zhang, Haocheng Feng, Junyu Han, Errui Ding, Gang Zeng, Jingdong Wang

Abstract: Detection Transformer (DETR) relies on one-to-one assignment for end-to-end object detection and lacks the capability of exploiting multiple positive object queries. We present a novel DETR training approach, named {\em Group DETR}, to support one-to-many assignment in a group-wise manner. To achieve it, we make simple modifications during training: (i) adopt $K$ groups of object queries; (ii) conduct decoder self-attention on each group of object queries with the same parameters; (iii) perform one-to-one assignment for each group, leading to $K$ positive object queries for each ground-truth object. In inference, we only use one group of object queries, making no modifications to model architectures and inference processes. Group DETR is a versatile training method and is applicable to various DETR variants. Our experiments show that Group DETR significantly speeds up the training convergences and improves the performances of various DETR-based methods.

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