Fashion Focus: Multi-modal Retrieval System for Video Commodity Localization in E-commerce
This system significantly simplifies the product-to-video matching process for e-commerce sellers, improving efficiency in product delivery via search and recommendation.
The paper addresses the manual matching of product images to video timestamps in e-commerce live-streams and short videos. They developed "Fashion Focus," a multi-modal retrieval system that automatically localizes product images within untrimmed videos by integrating visual, linguistic, and interaction context.
Nowadays, live-stream and short video shopping in E-commerce have grown exponentially. However, the sellers are required to manually match images of the selling products to the timestamp of exhibition in the untrimmed video, resulting in a complicated process. To solve the problem, we present an innovative demonstration of multi-modal retrieval system called "Fashion Focus", which enables to exactly localize the product images in the online video as the focuses. Different modality contributes to the community localization, including visual content, linguistic features and interaction context are jointly investigated via presented multi-modal learning. Our system employs two procedures for analysis, including video content structuring and multi-modal retrieval, to automatically achieve accurate video-to-shop matching. Fashion Focus presents a unified framework that can orientate the consumers towards relevant product exhibitions during watching videos and help the sellers to effectively deliver the products over search and recommendation.