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Hi-Fi: Hierarchical Feature Integration for Skeleton Detection


In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem. We present a new convolutional neural network (CNN) architecture by introducing a novel hierarchical feature integration mechanism, named Hi-Fi, to address the skeleton detection problem. The proposed CNN-based approach has a powerful multi-scale feature integration ability that intrinsically captures high-level semantics from deeper layers as well as low-level details from shallower layers. By hierarchically integrating different CNN feature levels with bidirectional guidance, our approach (1) enables mutual refinement across features of different levels, and (2) possesses the strong ability to capture both rich object context and high-resolution details. Experimental results show that our method significantly outperforms the state-of-the-art methods in terms of effectively fusing features from very different scales, as evidenced by a considerable performance improvement on several benchmarks.


Different multi-scale CNN feature fusing methods: (a) side-outputs as independent detectors at different scales; (b) deep-to-shallow refinement that brings high-level semantics to lower layers; (c) directly fuse all feature levels at once; (d) our hierarchical integration architecture, which enables bidirectional mutual refinement across low/high level features by recursive feature integration.

Performance Evaluation:

We test the proposed method on 4 skeleton detasets in terms of F-measure and pr-curve. The datasets are: SK-SMALL, SK-LARGE, SYM-PASCAL and WH-SYMMAX.

Performance comparison between different methods on four popular skeleton datasets. Our proposed Hi-Fi network outperforms other methods with an evident margin.

Pr-curve comparison ofskeleton detectors on different datasets.


If you use our model, please consider to cite paper below:

  title={Hi-Fi: Hierarchical Feature Integration for Skeleton Detection},
  author={Zhao, Kai and Shen, Wei and Gao, Shanghua and Li, Dandan and Cheng, Ming-Ming},
  journal={Preceding of the International Joint Conference on Artificial Intelligence, 2018},
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Code, Data and Pretrained Models:

Code will be available in

Square objects and detection results on SYM-PASCAL dataset

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