OKAEM is a unified learnable evolutionary framework that uses attention-based operators for pre-training on prior knowledge and real-time self-tuning adaptation.
In: Interna- tional Conference on Learning Representations (2019)
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GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.
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Learning Evolution via Optimization Knowledge Adaptation
OKAEM is a unified learnable evolutionary framework that uses attention-based operators for pre-training on prior knowledge and real-time self-tuning adaptation.
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Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.