Mutual Query Network for Multi-Modal Product Image Segmentation
Yun Guo Wei Feng Zheng Zhang Xiancong Ren Yaoyu Li Jingjing Lv Xin Zhu Zhangang Lin Jingping Shao
Product image segmentation is vital in e-commerce. Most existing methods
extract the product image foreground only based on the visual modality, making
it difficult to distinguish irrelevant products. As product titles contain
abundant appearance information and provide complementary cues for product
image segmentation, we propose a mutual query network to segment products based
on both visual and linguistic modalities. First, we design a language query
vision module to obtain the response of language description in image areas,
thus aligning the visual and linguistic representations across modalities.
Then, a vision query language module utilizes the correlation between visual
and linguistic modalities to filter the product title and effectively suppress
the content irrelevant to the vision in the title. To promote the research in
this field, we also construct a Multi-Modal Product Segmentation dataset
(MMPS), which contains 30,000 images and corresponding titles. The proposed
method significantly outperforms the state-of-the-art methods on MMPS.