Computer Vision and NLP for Multi-Task Fashion Modeling
Explore a 30-minute conference talk from Strange Loop Conference on multi-task fashion modeling using computer vision and natural language processing. Dive into Shoprunner's approach to standardizing attributes for millions of products from various retailers. Learn about the challenges of predicting attributes using different data representations like images, product descriptions, titles, and brand names. Discover the multi-task learning ensemble developed by Shoprunner's Data Science team, combining custom multi-task CNNs for image processing and fine-tuned BERT models for text classification in PyTorch. Gain insights into attribute modeling techniques, including multi-task network architectures, training protocols, and ensemble approaches. Explore topics such as clothing detection, segmentation, and the implementation of BERT for text analysis. Understand how to expand attribute modeling to new categories and the concept of multi-dataset multi-task learning. Presented by Michael Sugimura, a Senior Data Scientist at Shoprunner specializing in computer vision applications for e-commerce. Read more