Scale-Aware Trident Networks for Object Detection

Yanghao Li, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 6054-6063

Abstract


Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection. Based on the findings from the exploration experiments, we propose a novel Trident Network (TridentNet) aiming to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. As a bonus, a fast approximation version of TridentNet could achieve significant improvements without any additional parameters and computational cost compared with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP. Codes are available at https://git.io/fj5vR.

Related Material


[pdf]
[bibtex]
@InProceedings{Li_2019_ICCV,
author = {Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang},
title = {Scale-Aware Trident Networks for Object Detection},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}