An MLLM-guided architecture with a mixture of frequency experts and relational alignment loss achieves state-of-the-art all-in-one image restoration, outperforming prior methods by up to 1.35 dB on the CDD11 dataset.
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CogSENet proposes semantic-driven state space modules, bi-frequency fusion blocks, and continuous blur field estimation to outperform prior blind deblurring methods with fewer parameters.
A framework for multi-floor AGV trajectory planning that combines GVD-based task selection with optimization-based trajectory generation and constraint reduction, verified in simulation.
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Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts
An MLLM-guided architecture with a mixture of frequency experts and relational alignment loss achieves state-of-the-art all-in-one image restoration, outperforming prior methods by up to 1.35 dB on the CDD11 dataset.
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CogSENet: Blind Image Deblurring with Blur-Conditioned Semantic Routing and Explicit Frequency Fusion
CogSENet proposes semantic-driven state space modules, bi-frequency fusion blocks, and continuous blur field estimation to outperform prior blind deblurring methods with fewer parameters.
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Optimization-based Safe Trajectory Planning for Autonomous Ground Vehicle in Multi-Floor Scenarios
A framework for multi-floor AGV trajectory planning that combines GVD-based task selection with optimization-based trajectory generation and constraint reduction, verified in simulation.