# Fine-Grained Product Retrieval

PRISM-Net splits a product image's holistic features into a line pathway and a color pathway, supervises the two routers zero-shot with frozen SigLIP text prompts, and fuses both with an image-adaptive meta-router. In 5-fold cross-validation over 275,454 e-commerce images in 18 categories and full-scale retrieval of 55,091 queries against a 220,363-image gallery, the SwinV2 backbone gains 5.74 points in line mAP@10, while the color-only configuration gains 9.12 points in color mAP@10; on ConvNeXt-V2 and DINOv3, product retrieval and attribute alignment trade off.

PRISM-Net splits a product image's holistic features into a line pathway and a color pathway, supervises the two routers zero-shot with frozen SigLIP text prompts, and fuses both with an image-adaptive meta-router. In 5-fold cross-validation over 275,454 e-commerce images in 18 categories and full-scale retrieval of 55,091 queries against a 220,363-image gallery, the SwinV2 backbone gains 5.74 points in line mAP@10, while the color-only configuration gains 9.12 points in color mAP@10; on ConvNeXt-V2 and DINOv3, product retrieval and attribute alignment trade off.