Adaptive Linear Span Network for Object Skeleton Detection (2011.03972v1)

Abstract: 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 \href{https://github.com/sunsmarterjie/SDL-Skeleton}{\color{magenta}github.com/sunsmarterjie/SDL-Skeleton}.

• The paper introduces an Adaptive Linear Span Network that automates multi-scale feature fusion through neural architecture search and linear span theory.
• It employs a unit-pyramid search space combining linear span units and pyramids to harmonize shallow and deep features for enhanced detection.
• Experimental results show a 5.4% F-score improvement over state-of-the-art methods, demonstrating robustness across diverse datasets.

Summary of "Adaptive Linear Span Network for Object Skeleton Detection"

The paper presents a novel approach to object skeleton detection by proposing an Adaptive Linear Span Network (AdaLSN), which leverages Neural Architecture Search (NAS) to automatically integrate multi-scale features. The AdaLSN model is grounded in linear span theory, providing a theoretical basis for multi-scale deep feature fusion in neural networks.

AdaLSN addresses key challenges in skeleton detection, particularly the balance between detail preservation and semantic richness across different scales. Conventional methods often rely on manually crafted architectures, which, despite being infused with domain knowledge, struggle to optimize feature representation across diverse object scales and shapes. AdaLSN, contrarily, uses an automated approach to configure its architecture to ensure that features integrate complementarily across scales.

Key Features of AdaLSN

1. Neural Architecture Search (NAS): AdaLSN utilizes genetic algorithms to search for optimal network architectures. This automated search leads to architectures that adaptively respond to the complexities of scale-aware feature learning.
2. Linear Span Framework: The network is designed with the linear span theory, which provides a rationale for feature space expansion. This is achieved through the dynamic adaptation of network layers to enhance the complementary nature of extracted features.
3. Unit-Pyramid Search Space: The architecture consists of linear span units (LSUs) and a linear span pyramid (LSP), which together form a mixed search space. The LSUs transform input features, enhancing the subspace while the LSP ensures comprehensive integration of these expanded feature spaces.
4. Complementary Learning Strategy: This approach enforces the feature subspaces from shallower network stages to complement those from deeper stages, maximizing feature utility across different layers of the network.
5. Genetic Algorithm for Optimization: The search space is encoded into a genetic representation, allowing the use of genetic operations (e.g., crossover, mutation) to evolve network architectures that yield enhanced performance metrics.

Numerical Results and Implications

The experiments conducted using AdaLSN show significant improvements over existing state-of-the-art methods on a variety of object skeleton detection datasets. Specifically, AdaLSN reported an F-score improvement of up to 5.4% on benchmark datasets, demonstrating its effectiveness and robustness. Additionally, the model showcases versatility through successful applications to related tasks such as edge detection and road extraction.

Practical and Theoretical Implications

Theoretically, AdaLSN's use of linear span theory in the context of deep learning provides a new perspective on network design, emphasizing the importance of feature complementarity over multiple scales. Practically, this offers a robust method for object skeleton detection, which can be critical for various computer vision tasks including pose estimation and object localization.

Future Developments in AI

The adaptive approach of AdaLSN could be extended to other complex tasks in computer vision, potentially leading to breakthroughs in domains requiring nuanced feature representations across different scales. Future studies could explore integrating AdaLSN with other deep learning frameworks or apply similar NAS-driven span network strategies to tasks beyond computer vision, enhancing the versatility and efficacy of AI systems.