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Found 5 papers, 2 papers with code
Multistage Spatial Context Models for Learned Image Compression
The proposed method features a comparable decoding speed to Checkerboard while reaching the RD performance of Autoregressive and even also outperforming Autoregressive.
A comparative study of attention mechanism and generative adversarial network in facade damage segmentation
Attention mechanism and generative adversarial networks are two of the most popular strategies to improve the quality of semantic segmentation.
Streaming-capable High-performance Architecture of Learned Image Compression Codecs
Learned image compression allows achieving state-of-the-art accuracy and compression ratios, but their relatively slow runtime performance limits their usage.
Fast Crack Detection Using Convolutional Neural Network
To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints.
Crack Semantic Segmentation using the U-Net with Full Attention Strategy
Along with the rapid progress of deep learning technology, image semantic segmentation, an active research field, offers another solution, which is more effective and intelligent, to crack detection Through numerous artificial neural networks have been developed to address the preceding issue, corresponding explorations are never stopped improving the quality of crack detection.
