2 citations · 4 across the 5 of their papers we have counts for
5 papers
Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All
Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar +2
As online shopping is growing, the ability for buyers to virtually visualize products in their settings-a phenomenon we define as "Virtual Try-All"-has become crucial. Recent diffu…
Catalog Phrase Grounding (CPG): Grounding of Product Textual Attributes in Product Images for e-commerce Vision-Language Applications
Wenyi Wu, Karim Bouyarmane, Ismail Tutar
We present Catalog Phrase Grounding (CPG), a model that can associate product textual data (title, brands) into corresponding regions of product images (isolated product region, br…
DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling
Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar +2
We introduce DreamPaint, a framework to intelligently inpaint any e-commerce product on any user-provided context image. The context image can be, for example, the user's own image…
Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering
Tarik Arici, Kushal Kumar, Hayreddin Çeker +2
Price Per Unit (PPU) is an essential information for consumers shopping on e-commerce websites when comparing products. Finding total quantity in a product is required for computin…
MLIM: Vision-and-Language Model Pre-training with Masked Language and Image Modeling
Tarik Arici, Mehmet Saygin Seyfioglu, Tal Neiman +5
Vision-and-Language Pre-training (VLP) improves model performance for downstream tasks that require image and text inputs. Current VLP approaches differ on (i) model architecture (…