Selected work

Representative Papers

Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal

Aug 07, 2026

This work addresses the diverse and complex image degradations caused by adverse weather conditions, which severely impair visual system performance. Existing unified restoration methods often lack explicit spatial and semantic modeling of degradation characteristics. To overcome this limitation, we propose DCMPC-Net, which introduces cross-modal semantic prompting into image restoration for the first time. Our approach leverages a pretrained vision-language model to generate degradation-aware prompts and incorporates a prompt-guided attention alignment mechanism alongside a dual-path feature compensation strategy. This enables context-aware restoration and structural fidelity within a unified backbone architecture. Extensive experiments demonstrate that our method significantly outperforms current state-of-the-art techniques across multiple adverse weather conditions, achieving superior restoration accuracy and visual quality in both task-specific and unified evaluation settings.

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HPSO: Particle Swarm Optimization with Hypergraph-Based Topology

Aug 05, 2026

This work addresses the limitations of traditional particle swarm optimization (PSO), which relies on graph-based topologies supporting only pairwise interactions and thus struggles to capture higher-order population relationships, hindering exploration in complex search spaces. To overcome this, the study introduces hypergraphs into PSO for the first time, proposing a Hypergraph-based Particle Swarm Optimization (HPSO) algorithm. HPSO enables direct high-order interactions among multiple particles through hyperedges and incorporates an adaptive topology update mechanism that dynamically reconstructs the hypergraph structure based on cumulative average displacement, effectively preserving population diversity. Extensive experiments on the IEEE CEC’17 benchmark suite demonstrate that HPSO significantly outperforms both classical and state-of-the-art PSO variants, with ablation studies confirming its superior global search capability and overall effectiveness.

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Recent publications

Latest Papers

Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal

Aug 07, 2026

This work addresses the diverse and complex image degradations caused by adverse weather conditions, which severely impair visual system performance. Existing unified restoration methods often lack explicit spatial and semantic modeling of degradation characteristics. To overcome this limitation, we propose DCMPC-Net, which introduces cross-modal semantic prompting into image restoration for the first time. Our approach leverages a pretrained vision-language model to generate degradation-aware prompts and incorporates a prompt-guided attention alignment mechanism alongside a dual-path feature compensation strategy. This enables context-aware restoration and structural fidelity within a unified backbone architecture. Extensive experiments demonstrate that our method significantly outperforms current state-of-the-art techniques across multiple adverse weather conditions, achieving superior restoration accuracy and visual quality in both task-specific and unified evaluation settings.

0 citationsRead paper

HPSO: Particle Swarm Optimization with Hypergraph-Based Topology

Aug 05, 2026

This work addresses the limitations of traditional particle swarm optimization (PSO), which relies on graph-based topologies supporting only pairwise interactions and thus struggles to capture higher-order population relationships, hindering exploration in complex search spaces. To overcome this, the study introduces hypergraphs into PSO for the first time, proposing a Hypergraph-based Particle Swarm Optimization (HPSO) algorithm. HPSO enables direct high-order interactions among multiple particles through hyperedges and incorporates an adaptive topology update mechanism that dynamically reconstructs the hypergraph structure based on cumulative average displacement, effectively preserving population diversity. Extensive experiments on the IEEE CEC’17 benchmark suite demonstrate that HPSO significantly outperforms both classical and state-of-the-art PSO variants, with ablation studies confirming its superior global search capability and overall effectiveness.

0 citationsRead paper