DeepAI AI Chat
Log In Sign Up

Look, Read and Feel: Benchmarking Ads Understanding with Multimodal Multitask Learning

by   Huaizheng Zhang, et al.

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. It is exhausted to find relevant ads to match the provided content manually, 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 topic and sentiment of the ads. This motivates us to develop a novel deep multimodal multitask framework to integrate multiple modalities to achieve effective topic and sentiment prediction simultaneously for ads understanding. In particular, our model first extracts 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 train both the topic and sentiment prediction models jointly in an end-to-end manner. We conduct extensive experiments on the latest and large 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.


Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis

Multimodal machine learning is a core research area spanning the languag...

Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning

With the increasing popularity of video sharing websites such as YouTube...

Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

Multimodal fusion and multitask learning are two vital topics in machine...

Pedestrian Behavior Prediction via Multitask Learning and Categorical Interaction Modeling

Pedestrian behavior prediction is one of the major challenges for intell...

Disentangling Hate in Online Memes

Hateful and offensive content detection has been extensively explored in...

An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

Etsy is a global marketplace where people across the world connect to ma...

MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities

In this paper, we introduce the MLM (Multiple Languages and Modalities) ...