Scale-aware trident networks for object detection
Proceedings of the IEEE/CVF international conference on …, 2019•openaccess.thecvf.com
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 …
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 …
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.
openaccess.thecvf.com