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: For content-based image retrieval, features such as color, texture, shape, and sometimes more complex features like objects within the image are extracted from the images. These features help in comparing images to determine their similarity.

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Boy modeling encompasses a wide range of niches and specialties, including: : For content-based image retrieval, features such as

| Dataset | Conditioning Type | Metric (higher = better) | BOY | Baselines (cGAN, SPADE, DeepFill‑v2) | |---------|-------------------|--------------------------|-----|--------------------------------------| | | 5 random RGB points | FID ↓ 12.3 (BOY) vs. 24.7 (cGAN) / 21.1 (SPADE) | 12.3 | 24.7 / 21.1 | | COCO‑Stuff | 10 semantic keypoints | mIoU ↑ 0.68 vs. 0.45 (SPADE) / 0.51 (Pix2Pix) | 0.68 | 0.45 / 0.51 | | Cityscapes | 8 depth samples | LPIPS ↓ 0.112 vs. 0.209 (DeepFill‑v2) | 0.112 | 0.209 | | Real‑world sketches (user study) | Human‑drawn line art (≈ 30 strokes) | Mean Opinion Score 4.2/5 vs. 3.3 (SPADE) | 4.2 | 3.3 | Boy modeling encompasses a wide range of niches