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We present Catalog Phrase Grounding (CPG), a model that can associate producttextual data (title, brands) into corresponding regions of product images(isolated product region, brand logo region) for e-commerce vision-languageapplications. We use a state-of-the-art modulated multimodal transformerencoder-decoder architecture unifying object detection and phrase-grounding. Wetrain the model in self-supervised fashion with 2.3 million image-text pairssynthesized from an e-commerce site. The self-supervision data is annotatedwith high-confidence pseudo-labels generated with a combination of teachermodels: a pre-trained general domain phrase grounding model (e.g. MDETR) and aspecialized logo detection model. This allows CPG, as a student model, tobenefit from transfer knowledge from these base models combining general-domainknowledge and specialized knowledge. Beyond immediate catalog phrase groundingtasks, we can benefit from CPG representations by incorporating them as MLfeatures into downstream catalog applications that require deep semanticunderstanding of products. Our experiments on product-brand matching, achallenging e-commerce application, show that incorporating CPG representationsinto the existing production ensemble system leads to on average 5% recallimprovement across all countries globally (with the largest lift of 11% in asingle country) at fixed 95% precision, outperforming other alternativesincluding a logo detection teacher model and ResNet50.

Catalog Phrase Grounding (CPG): Grounding of Product Textual Attributes in Product Images for e-commerce Vision-Language Applications