709 research outputs found
AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation
Advertising posters, a form of information presentation, combine visual and
linguistic modalities. Creating a poster involves multiple steps and
necessitates design experience and creativity. This paper introduces
AutoPoster, a highly automatic and content-aware system for generating
advertising posters. With only product images and titles as inputs, AutoPoster
can automatically produce posters of varying sizes through four key stages:
image cleaning and retargeting, layout generation, tagline generation, and
style attribute prediction. To ensure visual harmony of posters, two
content-aware models are incorporated for layout and tagline generation.
Moreover, we propose a novel multi-task Style Attribute Predictor (SAP) to
jointly predict visual style attributes. Meanwhile, to our knowledge, we
propose the first poster generation dataset that includes visual attribute
annotations for over 76k posters. Qualitative and quantitative outcomes from
user studies and experiments substantiate the efficacy of our system and the
aesthetic superiority of the generated posters compared to other poster
generation methods.Comment: Accepted for ACM MM 202
ANALYSIS OF BEST PRACTICE OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION IN DIGITAL MARKETING ACTIVITIES
Rapid development of artificial intelligence is transforming the world we live in. Advancement in technology and consumer\u27s needs creates the urge for rapid adaptation by companies operating in a volatile and uncertain marketing environment in order to adequately shape their marketing decisions and achieve the best results on the market. The availability of information to consumers is greater than ever before causing an increase in the needs and demands they expect when buying and consuming a product or a service which results in higher efforts of personalization and individualization while creating marketing messages. This is precisely what innovative and disruptive technologies, such as intelligent self-learning systems based on artificial intelligence, allow companies to gain a better insight into the consumer\u27s needs and create marketing content that will result in higher engagement and conversion rates.
This study investigates and analyses set of examples of best practices of artificial intelligence implementation and the benefits of its usage in marketing activities and campaigns in automotive, retail and hospitality industry through predicting, testing and optimizing. Study shows the way artificial intelligence systems make an exceptional contribution to the optimization of marketing activities and overall marketing performance efficiency. The paper ends with the conclusions and recommendations how to implement some of the presented AI solutions into the Croatian business practice
TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design
Text design is one of the most critical procedures in poster design, as it
relies heavily on the creativity and expertise of humans to design text images
considering the visual harmony and text-semantic. This study introduces
TextPainter, a novel multimodal approach that leverages contextual visual
information and corresponding text semantics to generate text images.
Specifically, TextPainter takes the global-local background image as a hint of
style and guides the text image generation with visual harmony. Furthermore, we
leverage the language model and introduce a text comprehension module to
achieve both sentence-level and word-level style variations. Besides, we
construct the PosterT80K dataset, consisting of about 80K posters annotated
with sentence-level bounding boxes and text contents. We hope this dataset will
pave the way for further research on multimodal text image generation.
Extensive quantitative and qualitative experiments demonstrate that TextPainter
can generate visually-and-semantically-harmonious text images for posters.Comment: Accepted to ACM MM 2023. Dataset Link:
https://tianchi.aliyun.com/dataset/16003
Imagining machine vision: Four visual registers from the Chinese AI industry
Machine vision is one of the main applications of artificial intelligence. In China, the machine vision industry makes up more than a third of the national AI market, and technologies like face recognition, object tracking and automated driving play a central role in surveillance systems and social governance projects relying on the large-scale collection and processing of sensor data. Like other novel articulations of technology and society, machine vision is defined, developed and explained by different actors through the work of imagination. In this article, we draw on the concept of sociotechnical imaginaries to understand how Chinese companies represent machine vision. Through a qualitative multimodal analysis of the corporate websites of leading industry players, we identify a cohesive sociotechnical imaginary of machine vision, and explain how four distinct visual registers contribute to its articulation. These four registers, which we call computational abstraction, human–machine coordination, smooth everyday, and dashboard realism, allow Chinese tech companies to articulate their global ambitions and competitiveness through narrow and opaque representations of machine vision technologies.publishedVersio
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning
Digital human recommendation system has been developed to help customers find
their favorite products and is playing an active role in various recommendation
contexts. How to timely catch and learn the dynamics of the preferences of the
customers, while meeting their exact requirements, becomes crucial in the
digital human recommendation domain. We design a novel practical digital human
interactive recommendation agent framework based on Reinforcement Learning(RL)
to improve the efficiency of the interactive recommendation decision-making by
leveraging both the digital human features and the superior flexibility of RL.
Our proposed framework learns through real-time interactions between the
digital human and customers dynamically through the state-of-art RL algorithms,
combined with multimodal embedding and graph embedding, to improve the accuracy
of personalization and thus enable the digital human agent to timely catch the
attention of the customer. Experiments on real business data demonstrate that
our framework can provide better personalized customer engagement and better
customer experiences.Comment: 9 pages, 1 figure, 1 table, the paper has been accepted and this is
the final camera-ready for NeurIPS 2022 Workshop on Human in the Loop
Learning, https://neurips-hill.github.io
System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation
The malicious use of deep speech synthesis models may pose significant threat
to society. Therefore, many studies have emerged to detect the so-called
``deepfake audio". However, these studies focus on the binary detection of real
audio and fake audio. For some realistic application scenarios, it is needed to
know what tool or model generated the deepfake audio. This raises a question:
Can we recognize the system fingerprints of deepfake audio? Therefore, in this
paper, we propose a deepfake audio dataset for system fingerprint recognition
(SFR) and conduct an initial investigation. We collected the dataset from five
speech synthesis systems using the latest state-of-the-art deep learning
technologies, including both clean and compressed sets. In addition, to
facilitate the further development of system fingerprint recognition methods,
we give researchers some benchmarks that can be compared, and research
findings. The dataset will be publicly available.Comment: 12 pages, 3 figures. arXiv admin note: text overlap with
arXiv:2208.0964
Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
Ad creatives are one of the prominent mediums for online e-commerce
advertisements. Ad creatives with enjoyable visual appearance may increase the
click-through rate (CTR) of products. Ad creatives are typically handcrafted by
advertisers and then delivered to the advertising platforms for advertisement.
In recent years, advertising platforms are capable of instantly compositing ad
creatives with arbitrarily designated elements of each ingredient, so
advertisers are only required to provide basic materials. While facilitating
the advertisers, a great number of potential ad creatives can be composited,
making it difficult to accurately estimate CTR for them given limited real-time
feedback. To this end, we propose an Adaptive and Efficient ad creative
Selection (AES) framework based on a tree structure. The tree structure on
compositing ingredients enables dynamic programming for efficient ad creative
selection on the basis of CTR. Due to limited feedback, the CTR estimator is
usually of high variance. Exploration techniques based on Thompson sampling are
widely used for reducing variances of the CTR estimator, alleviating feedback
sparsity. Based on the tree structure, Thompson sampling is adapted with
dynamic programming, leading to efficient exploration for potential ad
creatives with the largest CTR. We finally evaluate the proposed algorithm on
the synthetic dataset and the real-world dataset. The results show that our
approach can outperform competing baselines in terms of convergence rate and
overall CTR
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
The 3rd Anti-UAV Workshop & Challenge aims to encourage research in
developing novel and accurate methods for multi-scale object tracking. The
Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released.
There are two main differences between this year's competition and the previous
two. First, we have expanded the existing dataset, and for the first time,
released a training set so that participants can focus on improving their
models. Second, we set up two tracks for the first time, i.e., Anti-UAV
Tracking and Anti-UAV Detection & Tracking. Around 76 participating teams from
the globe competed in the 3rd Anti-UAV Challenge. In this paper, we provide a
brief summary of the 3rd Anti-UAV Workshop & Challenge including brief
introductions to the top three methods in each track. The submission
leaderboard will be reopened for researchers that are interested in the
Anti-UAV challenge. The benchmark dataset and other information can be found
at: https://anti-uav.github.io/.Comment: Technical report for 3rd Anti-UAV Workshop and Challenge. arXiv admin
note: text overlap with arXiv:2108.0990
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