Scale-Aware Trident Networks for Object Detection
It addresses scale variation, a key challenge in object detection, with a novel architecture that improves accuracy without extra cost, though it is incremental in nature.
The paper tackles scale variation in object detection by proposing TridentNet, which uses parallel branches with different receptive fields and scale-aware training, achieving a state-of-the-art 48.4 mAP on COCO with ResNet-101.
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.