Fast Convergence of DETR with Spatially Modulated Co-Attention

作者：Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, Hongsheng Li · 发表于：2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 年份：2021 · DOI：10.1109/iccv48922.2021.00360 · 被引用次数：348 · 研究领域：Advanced Neural Network Applications、Domain Adaptation and Few-Shot Learning、Multimodal Machine Learning Applications

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 location-aware 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. Code is released at https://github.com/gaopengcuhk/SMCA-DETR.