Generating Persuasive Visual Storylines for Promotional Videos

Liu, Chang; Dong, Yi; Yu, Han; Shen, Zhiqi; Gao, Zhanning; Wang, Pan; Zhang, Changgong; Ren, Peiran; Xie, Xuansong; Cui, Lizhen; Miao, Chunyan

doi:10.1145/3357384.3357906

Cited by 9 publications

(22 citation statements)

References 28 publications

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“…We evaluate ShotCSP against the state-of-the-art algorithm WBP (Liu et al 2019) a method of evaluation is commonly adopted by related approaches (Choi, Oh, and So Kweon 2016;Zhong et al 2018;Liu et al 2019). The preference score of an algorithm a compared with algorithm b is calculated by P (a, b) =…”

Section: Evaluation and Discussionmentioning

confidence: 99%

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Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)

Liu

Dong

et al. 2020

AAAI

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There is an emerging trend for sellers to use videos to promote their products on e-commerce platforms such as Taobao.com. Current video production workflow includes the production of visual storyline by human directors. We propose a system to automatically generate visual storyline based on the input set of visual materials (e.g. video clips or still images) and then produce a promotional video. In particular, we propose an algorithm called Shot Composition, Selection and Plotting (ShotCSP), which generates visual storylines leveraging film-making principles to improve viewing experience and perceived persuasiveness.

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Section: Evaluation and Discussionmentioning

confidence: 99%

“…Specifically, an algorithm called Shot Composition, Selection and Plotting (ShotCSP) is proposed as a component in the system. A evaluation involving 96 users shows that ShotCSP outperforms the best state-of-theart method WBP (Liu et al 2019) with 61.16% higher viewing experience and 89.12% higher perceived persuasiveness.…”

Section: Introductionmentioning

confidence: 99%

Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)

Liu

Dong

et al. 2020

AAAI

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“…In future research, we plan to explore other optimization approaches such as memory usage and load balancing, in conjunction with search latency. We will also incorporate the considerations for fairness (Yu et al 2018), persuasiveness (Liu et al 2019) and privacy preservation (Gao et al 2019;Yang et al 2019) aspects related to search and recommendation to further enhance user experience.…”

Section: Discussionmentioning

confidence: 99%

Accelerating Ranking in E-Commerce Search Engines through Contextual Factor Selection

Zeng

et al. 2020

AAAI

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In large-scale search systems, the quality of the ranking results is continually improved with the introduction of more factors from complex procedures. Meanwhile, the increase in factors demands more computation resources and increases system response latency. It has been observed that, under some certain context a search instance may require only a small set of useful factors instead of all factors in order to return high quality results. Therefore, removing ineffective factors accordingly can significantly improve system efficiency. In this paper, we report our experience incorporating our Contextual Factor Selection (CFS) approach into the Taobao e-commerce platform to optimize the selection of factors based on the context of each search query in order to simultaneously achieve high quality search results while significantly reducing latency time. This problem is treated as a combinatorial optimization problem which can be tackled through a sequential decision-making procedure. The problem can be efficiently solved by CFS through a deep reinforcement learning method with reward shaping to address the problems of reward signal scarcity and wide reward signal distribution in real-world search engines. Through extensive off-line experiments based on data from the Taobao.com platform, CFS is shown to significantly outperform state-of-the-art approaches. Online deployment on Taobao.com demonstrated that CFS is able to reduce average search latency time by more than 40% compared to the previous approach with negligible reduction in search result quality. Under peak usage during the Single's Day Shopping Festival (November 11th) in 2017, CFS reduced peak load search latency time by 33% compared to the previous approach, helping Taobao.com achieve 40% higher revenue than the same period during 2016.

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“…Knowledge from Persuasive Theory. Liu et al [73] attempts to generate visual storylines that can maximize persuasiveness. According to the research on persuasive advertising [8], informativeness, attractiveness and emotion are key stimuli for achieving persuasion.…”

Section: Domainmentioning

confidence: 99%

“…Such a theoretical curve indicates the effect of over stimulation on viewers' experience. Liu et al [73] uses two transformed Sigmoid functions to translate the Wundt Curve into a machine learning model, in which the key parameters are learned via backpropagation. The LWC is also used in [76] to calculate persuasion scores.…”

Section: Domainmentioning

confidence: 99%

AI-Empowered Persuasive Video Generation: A Survey

Liu¹,

Han²

2021

Preprint

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Promotional videos are rapidly becoming a popular medium for persuading people to change their behaviours in many settings (e.g., online shopping, social enterprise initiatives). Today, such videos are often produced by professionals, which is a time-, labour-and cost-intensive undertaking. In order to produce such contents to support a large applications (e.g., e-commerce), the field of artificial intelligence (AI)-empowered persuasive video generation (AIPVG) has gained traction in recent years. This field is interdisciplinary in nature, which makes it challenging for new researchers to grasp. Currently, there is no comprehensive survey of AIPVG available. In this paper, we bridge this gap by reviewing key AI techniques that can be utilized to automatically generate persuasive videos. We offer a first-of-its-kind taxonomy which divides AIPVG into three major steps: 1) visual material understanding, which extracts information from the visual materials (VMs) relevant to the target of promotion; 2) visual storyline generation, which shortlists and arranges high-quality VMs into a sequence in order to compose a storyline with persuasive power; and 3) post-production, which involves background music generation and still image animation to enhance viewing experience. We also introduce the evaluation metrics and datasets commonly adopted in the field of AIPVG. We analyze the advantages and disadvantages of the existing works belonging to the above-mentioned steps, and discuss interesting potential future research directions.

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Generating Persuasive Visual Storylines for Promotional Videos

Cited by 9 publications

References 28 publications

Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)

Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)

Accelerating Ranking in E-Commerce Search Engines through Contextual Factor Selection

AI-Empowered Persuasive Video Generation: A Survey

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