Semantic Correspondence On Pf Willow

PCK

Results

Performance results of various models on this benchmark

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

0 of 8 row(s) selected.