  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.
