HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network
This work addresses the need for better multi-scale feature extraction in vision models, offering a flexible and efficient upgrade for existing networks, though it appears incremental as it builds upon residual blocks.
The paper introduces the Hierarchical-Split Block, a plug-and-play module for convolutional neural networks that enhances multi-scale feature representation, leading to improved performance on vision tasks such as image classification, object detection, and segmentation, with HS-ResNet50 achieving 81.28% top-1 accuracy on ImageNet-1k.
This paper addresses representational block named Hierarchical-Split Block, which can be taken as a plug-and-play block to upgrade existing convolutional neural networks, improves model performance significantly in a network. Hierarchical-Split Block contains many hierarchical split and concatenate connections within one single residual block. We find multi-scale features is of great importance for numerous vision tasks. Moreover, Hierarchical-Split block is very flexible and efficient, which provides a large space of potential network architectures for different applications. In this work, we present a common backbone based on Hierarchical-Split block for tasks: image classification, object detection, instance segmentation and semantic image segmentation/parsing. Our approach shows significant improvements over all these core tasks in comparison with the baseline. As shown in Figure1, for image classification, our 50-layers network(HS-ResNet50) achieves 81.28% top-1 accuracy with competitive latency on ImageNet-1k dataset. It also outperforms most state-of-the-art models. The source code and models will be available on: https://github.com/PaddlePaddle/PaddleClas