6 citations · 8 across the 4 of their papers we have counts for
4 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…
Domain Aligned CLIP for Few-shot Classification
Muhammad Waleed Gondal, Jochen Gast, Inigo Alonso Ruiz +4
Large vision-language representation learning models like CLIP have demonstrated impressive performance for zero-shot transfer to downstream tasks while largely benefiting from int…
Image-Text Pre-Training for Logo Recognition
Mark Hubenthal, Suren Kumar
Open-set logo recognition is commonly solved by first detecting possible logo regions and then matching the detected parts against an ever-evolving dataset of cropped logo images.…
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…