# Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

arXiv: https://arxiv.org/abs/1801.01849 · HTML: https://mlsanity.com/paper/1801.01849/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [Toward Robotic Weed Control: Detection of Nutsedge Weed in Bermudagrass Turf Using Inaccurate and Insufficient Training Data](https://mlsanity.com/paper/2106.08897/) (2021)
- [Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks](https://mlsanity.com/paper/2310.06437/) (2023)
- [Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection](https://mlsanity.com/paper/2103.10091/) (2021)
- [Mode-locking Theory for Long-Range Interaction in Artificial Neural Networks](https://mlsanity.com/paper/2303.05695/) (2023)
- [Topology-Aware Skeleton Detection via Lighthouse-Guided Structured Inference](https://mlsanity.com/paper/2604.20123/) (2026)
- [Depth-Dominant Skeleton Detection for Natural Scenes](https://mlsanity.com/paper/2608.16367/) (2026)

## Abstract (arXiv)

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.