Research map / Image generation / GANs
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TAGAN instead of AttnGAN | Text-guided image manipulation · CUB | Accuracy (average ranking): 2.19 -> 1.49 | Helped |
| Use TAGAN instead of AttnGAN | Text-guided image manipulation · CUB | Naturalness (average ranking): 2.11 -> 1.56 | Helped |
| Use TAGAN instead of AttnGAN | Text-guided image manipulation · CUB | L2 error: 0.25 -> 0.11 | Helped |
| Use TAGAN instead of AttnGAN | Text-guided image manipulation · Oxford-102 | Accuracy (average ranking): 2.21 -> 1.52 | Helped |
| Use TAGAN instead of AttnGAN | Text-guided image manipulation · Oxford-102 | Naturalness (average ranking): 2.10 -> 1.62 | Helped |
| Use TAGAN instead of SISGAN | Text-guided image manipulation · Oxford-102 | L2 error: 0.29 -> 0.11 | Helped |
| Use multi-scale conv3, conv4, and conv5 features rather than conv5 only (ablation) | TAGAN · Image manipulation | Qualitative attribute detail | Helped |
| Use multi-scale conv3, conv4, and conv5 features rather than conv3 only (ablation) | TAGAN · Image manipulation | Qualitative image quality | Helped |
Closest papers by meaning
- Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation (NeurIPS 2020 · similarity 0.93)
- ManiGAN: Text-Guided Image Manipulation (CVPR 2020 · similarity 0.91)
- Controllable Text-to-Image Generation (NeurIPS 2019 · similarity 0.90)
- Adversarial Learning of Semantic Relevance in Text to Image Synthesis (AAAI 2019 · similarity 0.89)
- Adversarial Text to Continuous Image Generation (CVPR 2024 · similarity 0.86)
- Progressive Text-to-Image Diffusion with Soft Latent Direction (AAAI 2024 · similarity 0.86)
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