Hateful Memes Detection via Complementary Visual and Linguistic Networks

• URL: http://arxiv.org/abs/2012.04977v1
• Date: Wed, 9 Dec 2020 11:11:09 GMT
• Title: Hateful Memes Detection via Complementary Visual and Linguistic Networks
• Authors: Weibo Zhang, Guihua Liu, Zhuohua Li, Fuqing Zhu
• Abstract summary: We investigate a solution based on complementary visual and linguistic network in Hateful Memes Challenge 2020. Both contextual-level and sensitive object-level information are considered in visual and linguistic embedding. Results show that CVL provides a decent performance, and produces 78:48% and 72:95% on the criteria of AUROC and Accuracy.
• Score: 4.229588654547344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hateful memes are widespread in social media and convey negative information. The main challenge of hateful memes detection is that the expressive meaning can not be well recognized by a single modality. In order to further integrate modal information, we investigate a candidate solution based on complementary visual and linguistic network in Hateful Memes Challenge 2020. In this way, more comprehensive information of the multi-modality could be explored in detail. Both contextual-level and sensitive object-level information are considered in visual and linguistic embedding to formulate the complex multi-modal scenarios. Specifically, a pre-trained classifier and object detector are utilized to obtain the contextual features and region-of-interests (RoIs) from the input, followed by the position representation fusion for visual embedding. While linguistic embedding is composed of three components, i.e., the sentence words embedding, position embedding and the corresponding Spacy embedding (Sembedding), which is a symbol represented by vocabulary extracted by Spacy. Both visual and linguistic embedding are fed into the designed Complementary Visual and Linguistic (CVL) networks to produce the prediction for hateful memes. Experimental results on Hateful Memes Challenge Dataset demonstrate that CVL provides a decent performance, and produces 78:48% and 72:95% on the criteria of AUROC and Accuracy. Code is available at https://github.com/webYFDT/hateful.

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