# Adaptive Linear Span Network for Object Skeleton Detection

arXiv: https://arxiv.org/abs/2011.03972 · HTML: https://mlsanity.com/paper/2011.03972/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions](https://mlsanity.com/paper/2203.06810/) (2022)
- [Context Attention Network for Skeleton Extraction](https://mlsanity.com/paper/2205.12066/) (2022)
- [Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks](https://mlsanity.com/paper/2310.06437/) (2023)
- [Exploring Complicated Search Spaces with Interleaving-Free Sampling](https://mlsanity.com/paper/2112.02488/) (2021)
- [Mode-locking Theory for Long-Range Interaction in Artificial Neural Networks](https://mlsanity.com/paper/2303.05695/) (2023)
- [MCMS: Multi-Category Information and Multi-Scale Stripe Attention for Blind Motion Deblurring](https://mlsanity.com/paper/2405.01083/) (2024)
- [Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via B'ezier Curve Modelling](https://mlsanity.com/paper/2406.13951/) (2024)
- [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)

Conventional networks for object skeleton detection are usually hand-crafted. Although effective, they require intensive priori knowledge to configure representative features for objects in different scale granularity.In this paper, we propose adaptive linear span network (AdaLSN), driven by neural architecture search (NAS), to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN is formulated with the theory of linear span, which provides one of the earliest explanations for multi-scale deep feature fusion. AdaLSN is materialized by defining a mixed unit-pyramid search space, which goes beyond many existing search spaces using unit-level or pyramid-level features.Within the mixed space, we apply genetic architecture search to jointly optimize unit-level operations and pyramid-level connections for adaptive feature space expansion. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off compared with state-of-the-arts. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction. Code is available at hrefhttps://github.com/sunsmarterjie/SDL-Skeletoncolormagentagithub.com/sunsmarterjie/SDL-Skeleton.