Long-Term Ad Memorability: Understanding & Generating Memorable Ads

Harini Si, Somesh Singh, Yaman Kumar Singla, Aanisha Bhattacharyya, Veeky Baths, Changyou Chen, Rajiv Ratn Shah, Balaji Krishnamurthy; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5707-5718

Despite the importance of long-term memory in marketing and brand building until now there has been no large-scale study on the memorability of ads. All previous memorability studies have been conducted on short-term recall on specific content types like action videos. On the other hand long-term memorability is crucial for advertising industry and ads are almost always highly multimodal. Therefore we release the first memorability dataset LAMBDA consisting of 1749 participants and 2205 ads covering 276 brands. Running statistical tests over different participant subpopulations and ad types we find many interesting insights into what makes an ad memorable e.g. fast-moving ads are more memorable than those with slower scenes; people who use ad-blockers remember a lower number of ads than those who don't. Next we present a model Henry to predict the memorability of a content. Henry achieves state-of-the-art performance across all prominent literature memorability datasets. It shows strong generalization performance with better results in 0-shot on unseen datasets. Finally with the intent of memorable ad generation we present a scalable method to build a high-quality memorable ad generation model by leveraging automatically annotated data. Our approach SEED (Self rEwarding mEmorability Modeling) starts with a language model trained on LAMBDA as seed data and progressively trains an LLM to generate more memorable ads. We show that the generated advertisements have 44% higher memorability scores than the original ads. We release this large-scale ad dataset UltraLAMBDA consisting of 5 million ads. Our code and the datasets LAMBDA and UltraLAMBDA are open-sourced at https://behavior-in-the-wild.github.io/memorability.