file added

_posts/2018/05/2018-05-10-machine_learn.md

@@ -0,0 +1,10 @@
+---
+layout: post
+subheadline: "Assessment"
+title: "Scale-Aware Trident Networks for Object Detection"
+date: 2018-08-10
+time: "12:00:00"
+authors: ["Zhaoxiang Zhang"]
+tags: ["machine", "coding", "impact", "assessment"]
+---
+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 this https URL.