SOTAVerified

Semantic correspondence

The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.

Papers

Showing 2650 of 175 papers

TitleStatusHype
Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social MediaCode1
Neural Matching Fields: Implicit Representation of Matching Fields for Visual CorrespondenceCode1
Learning Semantic Correspondence with Sparse AnnotationsCode1
PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative GroundingCode1
Doubly Deformable Aggregation of Covariance Matrices for Few-shot SegmentationCode1
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot SegmentationCode1
Semantic-Aware Fine-Grained CorrespondenceCode1
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross AttentionCode1
TransforMatcher: Match-to-Match Attention for Semantic CorrespondenceCode1
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence LearningCode1
CATs++: Boosting Cost Aggregation with Convolutions and TransformersCode1
Cost Aggregation Is All You Need for Few-Shot SegmentationCode1
Deep ViT Features as Dense Visual DescriptorsCode1
SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and RemovalCode1
Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in VideosCode1
Multi-scale Matching Networks for Semantic CorrespondenceCode1
Multi-Compound Transformer for Accurate Biomedical Image SegmentationCode1
Unsupervised Object-Level Representation Learning from Scene ImagesCode1
Color2Embed: Fast Exemplar-Based Image Colorization using Color EmbeddingsCode1
CATs: Cost Aggregation Transformers for Visual CorrespondenceCode1
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box SupervisionCode1
Convolutional Hough Matching NetworksCode1
PatentMatch: A Dataset for Matching Patent Claims & Prior ArtCode1
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained DataCode1
CoCosNet v2: Full-Resolution Correspondence Learning for Image TranslationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GeoAware-SC (Supervised, AP-10K P.T.)PCK85.6Unverified
2DINOv2PCK85.2Unverified
3GeoAware-SC (Supervised)PCK82.9Unverified
4SD+DINO (Supervised)PCK74.6Unverified
5GeoAware-SC + CleanDIFT (Zero-Shot)PCK70Unverified
6GeoAware-SC (Zero-Shot)PCK68.5Unverified
7SD+DINO + CleanDIFT (Zero-Shot)PCK64.8Unverified
8IFCATPCK64.4Unverified
9SD+DINO (Zero-shot)PCK64Unverified
10DIFT + CleanDIFT (Zero-Shot)PCK61.4Unverified
#ModelMetricClaimedVerifiedStatus
1DINOv2PCK95.8Unverified
2GeoAware-SC (Supervised, AP-10K P.T.)PCK95.7Unverified
3GeoAware-SC (Supervised)PCK95.1Unverified
4CATs++PCK93.8Unverified
5SD+DINO (Supervised)PCK93.6Unverified
6CATsPCK92.6Unverified
7VATPCK92.3Unverified
8VAT (ECCV)PCK92.3Unverified
9CHMPCK91.6Unverified
10DHPFPCK90.7Unverified
#ModelMetricClaimedVerifiedStatus
1LDMCorrespondencesPCK84.3Unverified
2VAT (ECCV)PCK81.6Unverified
3VATPCK81Unverified
4CHMPCK79.4Unverified
5CATsPCK79.2Unverified
6SCOTPCK78.1Unverified
7DHPFPCK77.6Unverified
8HPFPCK76.3Unverified
#ModelMetricClaimedVerifiedStatus
1HPFIoU63Unverified
2DHPFIoU62Unverified
#ModelMetricClaimedVerifiedStatus
1DINOv2PCK87.4Unverified
#ModelMetricClaimedVerifiedStatus
1LDM CorrespondencesMean PCK@0.0561.6Unverified