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Semantic Correspondence On Pf Pascal

Metrics

PCK

Results

Performance results of various models on this benchmark

Model Name	PCK	Paper Title	Repository
HPF	88.3	Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features	-
CHM	91.6	Convolutional Hough Matching Networks	-
SD+DINO (Supervised)	93.6	A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence	-
VAT	92.3	Cost Aggregation Is All You Need for Few-Shot Segmentation	-
DHPF	90.7	Learning to Compose Hypercolumns for Visual Correspondence	-
CATs++	93.8	CATs++: Boosting Cost Aggregation with Convolutions and Transformers	-
SCOT	88.8	Semantic Correspondence as an Optimal Transport Problem
NC-Net	-	Neighbourhood Consensus Networks	-
GeoAware-SC (Supervised)	95.1	Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence	-
GeoAware-SC (Zero-Shot)	82.6	Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence	-
VAT (ECCV)	92.3	Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation	-
GeoAware-SC (Supervised, AP-10K P.T.)	95.7	Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence	-
CATs	92.6	CATs: Cost Aggregation Transformers for Visual Correspondence	-
ANCNet	88.7	Correspondence Networks with Adaptive Neighbourhood Consensus	-

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Semantic Correspondence On Pf Pascal | SOTA | HyperAI