Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152993
Title: Look, read and feel : benchmarking ads understanding with multimodal multitask learning
Authors: Zhang, Huaizheng
Luo, Yong
Ai, Qiming
Wen, Yonggang
Hu, Han
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Zhang, H., Luo, Y., Ai, Q., Wen, Y. & Hu, H. (2020). Look, read and feel : benchmarking ads understanding with multimodal multitask learning. 28th ACM International Conference on Multimedia, 430-438. https://dx.doi.org/10.1145/3394171.3413582
Abstract: Given the massive market of advertising and the sharply increasing online multimedia content (such as videos), it is now fashionable to promote advertisements (ads) together with the multimedia content. However, manually finding relevant ads to match the provided content is labor-intensive, and hence some automatic advertising techniques are developed. Since ads are usually hard to understand only according to its visual appearance due to the contained visual metaphor, some other modalities, such as the contained texts, should be exploited for understanding. To further improve user experience, it is necessary to understand both the ads' topic and sentiment. This motivates us to develop a novel deep multimodal multitask framework that integrates multiple modalities to achieve effective topic and sentiment prediction simultaneously for ads understanding. In particular, in our framework termed Deep$M^2$Ad, we first extract multimodal information from ads and learn high-level and comparable representations. The visual metaphor of the ad is decoded in an unsupervised manner. The obtained representations are then fed into the proposed hierarchical multimodal attention modules to learn task-specific representations for final prediction. A multitask loss function is also designed to jointly train both the topic and sentiment prediction models in an end-to-end manner, where bottom-layer parameters are shared to alleviate over-fitting. We conduct extensive experiments on a large-scale advertisement dataset and achieve state-of-the-art performance for both prediction tasks. The obtained results could be utilized as a benchmark for ads understanding.
URI: https://hdl.handle.net/10356/152993
ISBN: 9781450379885
DOI: 10.1145/3394171.3413582
Rights: © 2020 Association for Computing Machinery. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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