The paper "Scale-Aware Trident Networks for Object Detection" addresses the challenge of scale variation in object detection. The authors conduct controlled experiments to investigate the impact of receptive fields on scale variation and propose a novel network architecture called Trident Network (TridentNet) to generate scale-specific feature maps with uniform representational power. TridentNet uses a parallel multi-branch architecture where each branch shares the same transformation parameters but has different receptive fields. A scale-aware training scheme is adopted to specialize each branch by sampling object instances of proper scales for training. The paper also introduces a fast approximation version of TridentNet, TridentNet Fast, which achieves significant improvements without additional parameters or computational cost. On the COCO dataset, TridentNet with a ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP. The contributions of the paper include investigating the effect of receptive fields, proposing TridentNet, and validating its effectiveness through thorough ablation studies and comparisons with state-of-the-art methods.The paper "Scale-Aware Trident Networks for Object Detection" addresses the challenge of scale variation in object detection. The authors conduct controlled experiments to investigate the impact of receptive fields on scale variation and propose a novel network architecture called Trident Network (TridentNet) to generate scale-specific feature maps with uniform representational power. TridentNet uses a parallel multi-branch architecture where each branch shares the same transformation parameters but has different receptive fields. A scale-aware training scheme is adopted to specialize each branch by sampling object instances of proper scales for training. The paper also introduces a fast approximation version of TridentNet, TridentNet Fast, which achieves significant improvements without additional parameters or computational cost. On the COCO dataset, TridentNet with a ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP. The contributions of the paper include investigating the effect of receptive fields, proposing TridentNet, and validating its effectiveness through thorough ablation studies and comparisons with state-of-the-art methods.