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Found 2 papers, 1 papers with code
Bag of Tricks: Semi-Supervised Cross-domain Crater Detection with Poor Data Quality
To obtain a better robust model with better cross-domain generalization in the presence of poor data quality, we propose the SCPQ model, in which we first propose a method for fusing shallow information using attention mechanism (FSIAM), which utilizes feature maps fused with deep convolved feature maps after fully extracting the global sensory field of shallow information via the attention mechanism module, which can fully fit the data to obtain a better sense of the domain in the presence of poor data, and thus better multiscale adaptability.
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Hyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands.
Ranked #1 on
Hyperspectral Image Classification
on Kennedy Space Center
(Overall Accuracy metric)
