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multi-branch convolution

Multi-branch convolution is a convolutional neural network architecture design in which an input feature map is routed through multiple parallel computational pathways, each applying distinct convolutional operations, before their outputs are merged together. Rather than applying a single uniform filter operation across an entire layer, a multi-branch layout typically incorporates varying kernel dimensions, dilation rates, pooling operations, or dimensionality-reducing bottleneck layers across its independent paths. This parallel configuration enables the network to process features across diverse spatial scales and receptive field sizes simultaneously, capturing both fine-grained local patterns and broader contextual information to enhance representational richness and computational efficiency.

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Receptive Field Block Net for Accurate and Fast Object Detection

Receptive Field Block Net for Accurate and Fast Object Detection

Songtao Liu, Di Huang, Yunhong Wang

OrganizationsAdvanced Innovation Center for Big Data and Brain ComputingBeihang University

Why you should read this

Introduces RFB Net, an object detector that incorporates biologically inspired receptive field structures into lightweight networks to match the accuracy of deep models at real-time speeds.

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at this https URL.

Added

2026-09-24