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Found 3 papers, 2 papers with code
Controllable Style Transfer via Test-time Training of Implicit Neural Representation
We propose a controllable style transfer framework based on Implicit Neural Representation that pixel-wisely controls the stylized output via test-time training.
ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization
We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views of an image, weighted by a confidence of pseudo-label, dubbed ConMatch.
Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence
In this paper, we propose a novel framework for jointly learning feature extraction and cost aggregation for semantic correspondence.
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