5 papers
Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation
Siliang Liu, Mohammad Ghasemi, Sapan Patel +1
Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent while offering upgraded benefits. Large language models (LLMs) can reason…
Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory
Hui Pang, Sunil Hadap, Violetta Shevchenko +2
Augmented reality is revolutionizing beauty industry with virtual try-on (VTO) applications, which empowers users to try a wide variety of products using their phones without the h…
Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
Siliang Liu, Rahul Suresh, Amin Banitalebi-Dehkordi
Accurate attribute extraction is critical for beauty product recommendations and building trust with customers. This remains an open problem, as existing solutions are often unreli…
Automated Material Properties Extraction For Enhanced Beauty Product Discovery and Makeup Virtual Try-on
Fatemeh Taheri Dezaki, Himanshu Arora, Rahul Suresh +1
The multitude of makeup products available can make it challenging to find the ideal match for desired attributes. An intelligent approach for product discovery is required to enha…
Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality
Parnian Afshar, Jenny Yeon, Andriy Levitskyy +2
We focus on addressing the challenges in responsible beauty product recommendation, particularly when it involves comparing the product's color with a person's skin tone, such as f…