TridentNet Block

A TridentNet Block is a feature extractor used in object detection models. Instead of feeding in multi-scale inputs like the image pyramid, in a TridentNet block we adapt the backbone network for different scales. These blocks create multiple scale-specific feature maps. With the help of dilated convolutions, different branches of trident blocks have the same network structure and share the same parameters yet have different receptive fields. Furthermore, to avoid training objects with extreme scales, a scale-aware training scheme is employed to make each branch specific to a given scale range matching its receptive field. Weight sharing is used to prevent overfitting.