RecursiveDet: End-to-End Region-based Recursive Object Detection
Jing Zhao Li Sun Qingli Li
End-to-end region-based object detectors like Sparse R-CNN usually have
multiple cascade bounding box decoding stages, which refine the current
predictions according to their previous results. Model parameters within each
stage are independent, evolving a huge cost. In this paper, we find the general
setting of decoding stages is actually redundant. By simply sharing parameters
and making a recursive decoder, the detector already obtains a significant
improvement. The recursive decoder can be further enhanced by positional
encoding (PE) of the proposal box, which makes it aware of the exact locations
and sizes of input bounding boxes, thus becoming adaptive to proposals from
different stages during the recursion. Moreover, we also design
centerness-based PE to distinguish the RoI feature element and dynamic
convolution kernels at different positions within the bounding box. To validate
the effectiveness of the proposed method, we conduct intensive ablations and
build the full model on three recent mainstream region-based detectors. The
RecusiveDet is able to achieve obvious performance boosts with even fewer model
parameters and slightly increased computation cost. Codes are available at
https://github.com/bravezzzzzz/RecursiveDet.