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Found 5 papers, 1 papers with code
Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All
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.
Domain Aligned CLIP for Few-shot Classification
Large vision-language representation learning models like CLIP have demonstrated impressive performance for zero-shot transfer to downstream tasks while largely benefiting from inter-modal (image-text) alignment via contrastive objectives.
Image-Text Pre-Training for Logo Recognition
The matching model, a metric learning problem, is especially challenging for logo recognition due to the mixture of text and symbols in logos.
DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling
We introduce DreamPaint, a framework to intelligently inpaint any e-commerce product on any user-provided context image.
Spatiotemporal Articulated Models for Dynamic SLAM
We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations.
