Yunfan Ye1, Fang Liu1, *, Shan Zhao2, Wanting Hu3, Zhiyao Liang4
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1361-1372, 2020, DOI:10.32604/cmc.2020.011632
Abstract By efficiently and accurately predicting the adoptability of pets, shelters and
rescuers can be positively guided on improving attraction of pet profiles, reducing animal
suffering and euthanization. Previous prediction methods usually only used a single type
of content for training. However, many pets contain not only textual content, but also
images. To make full use of textual and visual information, this paper proposed a novel
method to process pets that contain multimodal information. We employed several CNN
(Convolutional Neural Network) based models and other methods to extract features from
images and texts to obtain the initial multimodal representation, then reduce… More >
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