Research map / Detection and segmentation / Weakly supervised detection and segmentation
SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SSAT instead of the strongest baseline, PSGAN | Makeup transfer · User study | Rank 1 selection rate: 11.3% -> 82.1% | Helped |
| Use SSAT instead of SCGAN | Makeup transfer · User study | Rank 1 selection rate: 3.6% -> 82.1% | Helped |
| Use SSAT instead of BeautyGAN | Makeup transfer · User study | Rank 1 selection rate: 3.0% -> 82.1% | Helped |
| Add SSCFT for semantic correspondence (ablation) | SSAT makeup transfer · Makeup transfer | Visual similarity to reference makeup | Helped |
| Add L_sem and face parsing (ablation) | SSAT makeup transfer · Makeup transfer | Semantic alignment | Helped |
Closest papers by meaning
- Diffusion-Based Makeup Transfer with Facial Region-Aware Makeup Features (CVPR 2026 · similarity 0.87)
- PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer (CVPR 2020 · similarity 0.86)
- Spatially-Invariant Style-Codes Controlled Makeup Transfer (CVPR 2021 · similarity 0.85)
- SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models (NeurIPS 2024 · similarity 0.85)
- Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth (CVPR 2024 · similarity 0.84)
- S2WAT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention (AAAI 2024 · similarity 0.82)
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