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Found 4 papers, 3 papers with code
Semantic-Aware Dynamic Parameter for Video Inpainting Transformer
Recent learning-based video inpainting approaches have achieved considerable progress.
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods.
Self-Supervised Adaptation for Video Super-Resolution
Recent single-image super-resolution (SISR) networks, which can adapt their network parameters to specific input images, have shown promising results by exploiting the information available within the input data as well as large external datasets.
Fast Adaptation to Super-Resolution Networks via Meta-Learning
In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time.
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