SOTAVerified

Semantic correspondence

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 175 papers

TitleStatusHype
A Framework For Image Synthesis Using Supervised Contrastive Learning—0
Distillation of Diffusion Features for Semantic Correspondence—0
Multi-Level Correlation Network For Few-Shot Image ClassificationCode0
OmniCreator: Self-Supervised Unified Generation with Universal Editing—0
Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence—0
DreamMover: Leveraging the Prior of Diffusion Models for Image Interpolation with Large Motion—0
MOSMOS: Multi-organ segmentation facilitated by medical report supervision—0
CONDA: Condensed Deep Association Learning for Co-Salient Object Detection—0
SGOR: Outlier Removal by Leveraging Semantic and Geometric Information for Robust Point Cloud RegistrationCode0
GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding—0
Advanced Multimodal Deep Learning Architecture for Image-Text Matching—0
Eye-for-an-eye: Appearance Transfer with Semantic Correspondence in Diffusion Models—0
Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform—0
Independently Keypoint Learning for Small Object Semantic Correspondence—0
Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation—0
FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fields—0
Pixel-level Semantic Correspondence through Layout-aware Representation Learning and Multi-scale Matching IntegrationCode0
Making Visual Sense of Oracle Bones for You and MeCode0
Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps—0
CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer—0
Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired ImagesCode0
GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence—0
TPDR: A Novel Two-Step Transformer-based Product and Class Description Match and Retrieval Method—0
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation—0
Learning From Noisy Correspondence With Tri-Partition for Cross-Modal Matching—0
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All datasetsSPair-71kPF-PASCALPF-WILLOWCaltech-101AP-10KCUB-200-2011

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GeoAware-SC (Supervised, AP-10K P.T.)PCK85.6—Unverified
2DINOv2PCK85.2—Unverified
3GeoAware-SC (Supervised)PCK82.9—Unverified
4SD+DINO (Supervised)PCK74.6—Unverified
5GeoAware-SC + CleanDIFT (Zero-Shot)PCK70—Unverified
6GeoAware-SC (Zero-Shot)PCK68.5—Unverified
7SD+DINO + CleanDIFT (Zero-Shot)PCK64.8—Unverified
8IFCATPCK64.4—Unverified
9SD+DINO (Zero-shot)PCK64—Unverified
10DIFT + CleanDIFT (Zero-Shot)PCK61.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DINOv2PCK95.8—Unverified
2GeoAware-SC (Supervised, AP-10K P.T.)PCK95.7—Unverified
3GeoAware-SC (Supervised)PCK95.1—Unverified
4CATs++PCK93.8—Unverified
5SD+DINO (Supervised)PCK93.6—Unverified
6CATsPCK92.6—Unverified
7VATPCK92.3—Unverified
8VAT (ECCV)PCK92.3—Unverified
9CHMPCK91.6—Unverified
10DHPFPCK90.7—Unverified
#ModelMetricClaimedVerifiedStatus
1LDMCorrespondencesPCK84.3—Unverified
2VAT (ECCV)PCK81.6—Unverified
3VATPCK81—Unverified
4CHMPCK79.4—Unverified
5CATsPCK79.2—Unverified
6SCOTPCK78.1—Unverified
7DHPFPCK77.6—Unverified
8HPFPCK76.3—Unverified
#ModelMetricClaimedVerifiedStatus
1HPFIoU63—Unverified
2DHPFIoU62—Unverified
#ModelMetricClaimedVerifiedStatus
1DINOv2PCK87.4—Unverified
#ModelMetricClaimedVerifiedStatus
1LDM CorrespondencesMean PCK@0.0561.6—Unverified