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Found 9 papers, 2 papers with code
Salient Object-Aware Background Generation using Text-Guided Diffusion Models
Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments.
Staging E-Commerce Products for Online Advertising using Retrieval Assisted Image Generation
In addition, we show how our staging approach can enable animations of moving products leading to a video ad from a product image.
TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity
To realize this opportunity, we propose an ad text strength indicator (TSI) which: (i) predicts the click-through-rate (CTR) for an input ad text, (ii) fetches similar existing ads to create a neighborhood around the input ad, (iii) and compares the predicted CTRs in the neighborhood to declare whether the input ad is strong or weak.
VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search
Motivated by our observations in logged data on ad image search queries (given ad text), we formulate a keyword extraction problem, where a keyword extracted from the ad text (or its augmented version) serves as the ad image query.
Powering COVID-19 community Q&A with Curated Side Information
In this work, we demonstrate the effectiveness of different attention based neural models that can directly exploit side information available in technical documents or verified forums (e. g., research publications on COVID-19 or WHO website).
Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement
Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers.
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
There is a perennial need in the online advertising industry to refresh ad creatives, i. e., images and text used for enticing online users towards a brand.
Learning from Multi-User Activity Trails for B2B Ad Targeting
Online purchase decisions in organizations can go through a complex journey with multiple agents involved in the decision making process.
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach
The policy based on this state space is trained on past decisions and outcomes via a novel Q-learning algorithm which accounts for the delay in install notifications.
