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Representative Papers

Human-inspired Global-to-Parallel Multi-scale Encoding for Lightweight Vision Models

Jan 13, 2026

Existing lightweight vision models struggle to balance parameter count, computational cost, and performance, while inadequately modeling human visual mechanisms. Inspired by the human visual system’s tendency to process scenes holistically before focusing on details—and to retain global context even during local attention—this work proposes a Global-to-Parallel Multi-scale Encoding (GPM) mechanism and introduces H-GPE, a lightweight network architecture. H-GPE employs a Global Insight Generator (GIG) to capture holistic semantics, while parallel branches concurrently model mid-to-large scale relationships and fine-grained textures, enabling synergistic integration of global and local features. Evaluated across image classification, object detection, and semantic segmentation tasks, H-GPE consistently outperforms state-of-the-art lightweight models with significantly fewer FLOPs and parameters, achieving a superior trade-off between accuracy and efficiency.

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A many-objective evolutionary algorithm using indicator-driven weight vector optimization

Oct 03, 2025

Fixed-weight-vector approaches in MOEA/D suffer from poor convergence and uneven solution distribution when handling irregular Pareto fronts—e.g., degenerate, discontinuous, or inverted fronts. Method: This paper proposes an indicator-driven adaptive weight vector optimization algorithm, integrating the MOEA/D decomposition framework with a simplified hypervolume (S-HV) indicator. Under guidance from the R2 indicator, the algorithm dynamically adjusts the frequency of weight vector updates to enable real-time improvement of solution distribution. Contribution/Results: The key innovation lies in deeply embedding indicator-based evaluation into the weight vector evolution process, thereby jointly optimizing convergence and diversity. Experimental results on 12 irregular benchmark problems demonstrate that the proposed algorithm significantly outperforms six state-of-the-art multi-objective evolutionary algorithms, exhibiting superior robustness and search efficiency—particularly on complex front geometries.

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Recent publications

Latest Papers

Human-inspired Global-to-Parallel Multi-scale Encoding for Lightweight Vision Models

Jan 13, 2026

Existing lightweight vision models struggle to balance parameter count, computational cost, and performance, while inadequately modeling human visual mechanisms. Inspired by the human visual system’s tendency to process scenes holistically before focusing on details—and to retain global context even during local attention—this work proposes a Global-to-Parallel Multi-scale Encoding (GPM) mechanism and introduces H-GPE, a lightweight network architecture. H-GPE employs a Global Insight Generator (GIG) to capture holistic semantics, while parallel branches concurrently model mid-to-large scale relationships and fine-grained textures, enabling synergistic integration of global and local features. Evaluated across image classification, object detection, and semantic segmentation tasks, H-GPE consistently outperforms state-of-the-art lightweight models with significantly fewer FLOPs and parameters, achieving a superior trade-off between accuracy and efficiency.

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A many-objective evolutionary algorithm using indicator-driven weight vector optimization

Oct 03, 2025

Fixed-weight-vector approaches in MOEA/D suffer from poor convergence and uneven solution distribution when handling irregular Pareto fronts—e.g., degenerate, discontinuous, or inverted fronts. Method: This paper proposes an indicator-driven adaptive weight vector optimization algorithm, integrating the MOEA/D decomposition framework with a simplified hypervolume (S-HV) indicator. Under guidance from the R2 indicator, the algorithm dynamically adjusts the frequency of weight vector updates to enable real-time improvement of solution distribution. Contribution/Results: The key innovation lies in deeply embedding indicator-based evaluation into the weight vector evolution process, thereby jointly optimizing convergence and diversity. Experimental results on 12 irregular benchmark problems demonstrate that the proposed algorithm significantly outperforms six state-of-the-art multi-objective evolutionary algorithms, exhibiting superior robustness and search efficiency—particularly on complex front geometries.

0 citationsRead paper