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pro vyhledávání: '"Sheng, Jiamu"'
The effectiveness and efficiency of modeling complex spectral-spatial relations are both crucial for Hyperspectral image (HSI) classification. Most existing methods based on CNNs and transformers still suffer from heavy computational burdens and have
Externí odkaz:
http://arxiv.org/abs/2406.07050
Development of multimodal models has marked a significant step forward in how machines understand videos. These models have shown promise in analyzing short video clips. However, when it comes to longer formats like movies, they often fall short. The
Externí odkaz:
http://arxiv.org/abs/2403.01422
The effectiveness of spectral-spatial feature learning is crucial for the hyperspectral image (HSI) classification task. Diffusion models, as a new class of groundbreaking generative models, have the ability to learn both contextual semantics and tex
Externí odkaz:
http://arxiv.org/abs/2306.08964
Despite substantial progress in no-reference image quality assessment (NR-IQA), previous training models often suffer from over-fitting due to the limited scale of used datasets, resulting in model performance bottlenecks. To tackle this challenge, w
Externí odkaz:
http://arxiv.org/abs/2302.09838
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Learning effective spectral-spatial features is important for the hyperspectral image (HSI) classification task, but the majority of existing HSI classification methods still suffer from modeling complex spectral-spatial relations and characterizing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8fb2361954603f4457acf49412a2a6a