DECO: Query-Based End-to-End Object Detection with ConvNets
Xinghao Chen Siwei Li Yijing Yang Yunhe Wang
Detection Transformer (DETR) and its variants have shown great potential for
accurate object detection in recent years. The mechanism of object query
enables DETR family to directly obtain a fixed number of object predictions and
streamlines the detection pipeline. Meanwhile, recent studies also reveal that
with proper architecture design, convolution networks (ConvNets) also achieve
competitive performance with transformers, \eg, ConvNeXt. To this end, in this
paper we explore whether we could build a query-based end-to-end object
detection framework with ConvNets instead of sophisticated transformer
architecture. The proposed framework, \ie, Detection ConvNet (DECO), is
composed of a backbone and convolutional encoder-decoder architecture. We
carefully design the DECO encoder and propose a novel mechanism for our DECO
decoder to perform interaction between object queries and image features via
convolutional layers. We compare the proposed DECO against prior detectors on
the challenging COCO benchmark. Despite its simplicity, our DECO achieves
competitive performance in terms of detection accuracy and running speed.
Specifically, with the ResNet-50 and ConvNeXt-Tiny backbone, DECO obtains
$38.6\%$ and $40.8\%$ AP on COCO \textit{val} set with $35$ and $28$ FPS
respectively and outperforms the DETR model. Incorporated with advanced
multi-scale feature module, our DECO+ achieves $47.8\%$ AP with $34$ FPS. We
hope the proposed DECO brings another perspective for designing object
detection framework.