HyperAI

Semantic Correspondence On Spair 71K

Metriken

PCK

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
PCK
Paper TitleRepository
HPF28.2Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features-
GeoAware-SC (Zero-Shot)68.5Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
GeoAware-SC (Supervised, AP-10K P.T.)85.6Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
CATs++59.8CATs++: Boosting Cost Aggregation with Convolutions and Transformers
GeoAware-SC (Supervised)82.9Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
SCOT35.6Semantic Correspondence as an Optimal Transport Problem
GeoAware-SC + CleanDIFT (Zero-Shot)70.0CleanDIFT: Diffusion Features without Noise-
VAT54.2Cost Aggregation Is All You Need for Few-Shot Segmentation
SD+DINO (Supervised)74.6A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
VAT (ECCV)55.5Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
SD+DINO (Zero-shot)64.0A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
MMNet50.4Multi-scale Matching Networks for Semantic Correspondence
IFCAT64.4Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence-
DHPF37.3Learning to Compose Hypercolumns for Visual Correspondence
ANCNet30.1Correspondence Networks with Adaptive Neighbourhood Consensus
LDMCorrespondences45.4Unsupervised Semantic Correspondence Using Stable Diffusion
SD+DINO + CleanDIFT (Zero-Shot)64.8CleanDIFT: Diffusion Features without Noise-
CATs49.9CATs: Cost Aggregation Transformers for Visual Correspondence
DIFT + CleanDIFT (Zero-Shot)61.4CleanDIFT: Diffusion Features without Noise-
CHM46.3Convolutional Hough Matching Networks
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