Semantic Correspondence On Pf Willow

PCK

Results

Performance results of various models on this benchmark

		Paper Title
LDMCorrespondences	84.3	Unsupervised Semantic Correspondence Using Stable Diffusion
VAT (ECCV)	81.6	Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
VAT	81.0	Cost Aggregation Is All You Need for Few-Shot Segmentation
CHM	79.4	Convolutional Hough Matching Networks
CATs	79.2	CATs: Cost Aggregation Transformers for Visual Correspondence
SCOT	78.1	Semantic Correspondence as an Optimal Transport Problem
DHPF	77.6	Learning to Compose Hypercolumns for Visual Correspondence
HPF	76.3	Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features

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