Chrysanthemum image quality assessment via multi-scale feature fusion and meta-learning
- 2 February 2026
- journal article
- Published by Elsevier in Expert Systems with Applications
- Vol. 312
Abstract
No abstract availableFunding Information
- Ministry of Agriculture and Rural Affairs of the People's Republic of China (CARS-21)
- Qinglan Project of Jiangsu Province of China
- Project 333 of Jiangsu Province
- Government of Jiangsu Province
- Agriculture Research System of China
- National Natural Science Foundation of China (62101268)
- National Natural Science Foundation of China (82204770)
This publication has 41 references indexed in Scilit:
- Bidirectional feature fusion via cross-attention transformer for chrysanthemum classificationPattern Analysis and Applications, 2025
- Perceptual video quality assessment: a surveyScience China Information Sciences, 2024
- Combining CNN and transformers for full-reference and no-reference image quality assessmentNeurocomputing, 2023
- Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database TrainingIEEE Journal of Selected Topics in Signal Processing, 2023
- MAML2: meta reinforcement learning via meta-learning for task categoriesFrontiers of Computer Science, 2022
- Contrastive distortion‐level learning‐based no‐reference image‐quality assessmentInternational Journal of Intelligent Systems, 2022
- Cross-Frame Transformer-Based Spatio-Temporal Video Super-ResolutionIEEE Transactions on Broadcasting, 2022
- Perceptual image quality assessment: a surveyScience China Information Sciences, 2020
- Blind Image Quality Estimation via Distortion AggravationIEEE Transactions on Broadcasting, 2018
- Meta-learning for evolutionary parameter optimization of classifiersMachine Learning, 2012