mlsanityData to 14 Oct 2026

Semantic Attribute Matching Networks

arXiv 1904.02969 ↗First posted 5 Apr 2019cs.CVcited by 20

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Abstract (arXiv)

We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations. SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences. To learn the networks using weak supervisions in the form of image pairs, we present a semantic attribute matching loss based on the matching similarity between an attribute transferred source feature and a warped target feature. With SAM-Net, the state-of-the-art performance is attained on several benchmarks for semantic matching and attribute transfer.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Apr 2019.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 3; 2024: 0; 2025: 4; 2026: 1.

20 papers.

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  1. 2021Learning Accurate Dense Correspondences and When to Trust Themcited by 113
  2. 2023Telling Left from Right: Identifying Geometry-Aware Semantic Correspondencecited by 86
  3. 2021PDC-Net+: Enhanced Probabilistic Dense Correspondence Networkcited by 80
  4. 2021CATs: Cost Aggregation Transformers for Visual Correspondencecited by 77
  5. 2022CATs++: Boosting Cost Aggregation with Convolutions and Transformerscited by 53
  6. 2021Warp Consistency for Unsupervised Learning of Dense Correspondencescited by 42
  7. 2021Deep Lucas-Kanade Homography for Multimodal Image Alignmentcited by 38
  8. 2022Learning Semantic Correspondence with Sparse Annotationscited by 31
  9. 2021Deep Matching Prior: Test-Time Optimization for Dense Correspondencecited by 30
  10. 2022Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondencescited by 30
  11. 2023ASIC: Aligning Sparse in-the-wild Image Collectionscited by 27
  12. 2022Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labelscited by 16
8 more
  1. 2025Do It Yourself: Learning Semantic Correspondence from Pseudo-Labelscited by 13
  2. 2025MATCHA:Towards Matching Anythingcited by 12
  3. 2021Deep Translation Prior: Test-time Training for Photorealistic Style Transfercited by 8
  4. 2025Semantic Correspondence: Unified Benchmarking and a Strong Baselinecited by 3
  5. 2025Towards Geometry-Grounded Dense Semantic Matching with VGGT Priorscited by 3
  6. 2026MARCO: Navigating the Unseen Space of Semantic Correspondencecited by 3
  7. 2023Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Imagescited by 2
  8. 2022Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolutioncited by 0