Fast Convergence of DETR with Spatially Modulated Co-Attention

Peng Gao¹     Minghang Zheng³     Xiaogang Wang²     Jifeng Dai⁴     Hongsheng Li²

¹Shanghai AI Laboratory,

²CUHK-SenseTime Joint Laboratory, The Chinese University of Hong Kong

³Peking University     ⁴SenseTime Research

1155102382@link.cuhk.edu.hk     hsli@ee.cuhk.edu.hk

Abstract:

The recently proposed Detection Transformer (DETR)model successfully applies Transformer to objects detection and achieves comparable performance with two-stage object detection frameworks, such as Faster-RCNN. However, DETR suffers from its slow convergence. Training DETR [4] from scratch needs 500 epochs to achieve a high accuracy. To accelerate its convergence, we propose a simple yet effective scheme for improving the DETR framework, namely Spatially Modulated Co-Attention (SMCA) mechanism. The core idea of SMCA is to conduct locationaware co-attention in DETR by constraining co-attention responses to be high near initially estimated bounding box locations. Our proposed SMCA increases DETR’s convergence speed by replacing the original co-attention mechanism in the decoder while keeping other operations in DETR unchanged. Furthermore, by integrating multi-head and scale-selection attention designs into SMCA, our fully fledged SMCA can achieve better performance compared to DETR with a dilated convolution-based backbone (45.6 mAP at 108 epochs vs. 43.3 mAP at 500 epochs). We perform extensive ablation studies on COCO dataset to validate SMCA.