SOTAVerified

Semantic correspondence

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

Papers

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Showing 1–10 of 175 papers

TitleStatusHype
RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control—0
Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence—0
Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels—0
MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation—0
Cora: Correspondence-aware image editing using few step diffusionCode1
Semantic Correspondence: Unified Benchmarking and a Strong BaselineCode1
TC-MGC: Text-Conditioned Multi-Grained Contrastive Learning for Text-Video RetrievalCode0
SemAlign3D: Semantic Correspondence between RGB-Images through Aligning 3D Object-Class Representations—0
Semantix: An Energy Guided Sampler for Semantic Style Transfer—0
Evaluating book summaries from internal knowledge in Large Language Models: a cross-model and semantic consistency approach—0
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All datasetsSPair-71kPF-PASCALPF-WILLOWCaltech-101AP-10KCUB-200-2011

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HPFIoU63—Unverified
2DHPFIoU62—Unverified