Online multimedia advertising

Master'sMASTER OF SCIENC

Evaluating Content-centric vs User-centric Ad Affect Recognition

Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation (ICMI) 201

Affect Recognition in Ads with Application to Computational Advertising

Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201

Annotating, Understanding, and Predicting Long-term Video Memorability

International audienceMemorability can be regarded as a useful metric of video importance to help make a choice between competing videos. Research on computational understanding of video memorability is however in its early stages. There is no available dataset for modelling purposes, and the few previous attempts provided protocols to collect video memorability data that would be difficult to generalize. Furthermore, the computational features needed to build a robust memorability predictor remain largely undiscovered. In this article, we propose a new protocol to collect long-term video memorability annotations. We measure the memory performances of 104 participants from weeks to years after memorization to build a dataset of 660 videos for video memorability prediction. This dataset is made available for the research community. We then analyze the collected data in order to better understand video memorability, in particular the effects of response time, duration of memory retention and repetition of visualization on video memorability. We finally investigate the use of various types of audio and visual features and build a computational model for video memorability prediction. We conclude that high level visual semantics help better predict the memorability of videos

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

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.Comment: 8 pages, 5 figure