mlsanityData to 7 Oct 2026

Towards Reliable Advertising Image Generation Using Human Feedback

arXiv 2408.00418 ↗First posted 1 Aug 2024cs.CVcited by 15

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Abstract (arXiv)

In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention. While generative models automate image generation, they often produce substandard images that may mislead customers and require significant labor costs to inspect. This paper delves into increasing the rate of available generated images. We first introduce a multi-modal Reliable Feedback Network (RFNet) to automatically inspect the generated images. Combining the RFNet into a recurrent process, Recurrent Generation, results in a higher number of available advertising images. To further enhance production efficiency, we fine-tune diffusion models with an innovative Consistent Condition regularization utilizing the feedback from RFNet (RFFT). This results in a remarkable increase in the available rate of generated images, reducing the number of attempts in Recurrent Generation, and providing a highly efficient production process without sacrificing visual appeal. We also construct a Reliable Feedback 1 Million (RF1M) dataset which comprises over one million generated advertising images annotated by human, which helps to train RFNet to accurately assess the availability of generated images and faithfully reflect the human feedback. Generally speaking, our approach offers a reliable solution for advertising image generation.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 57 of its 68 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

57 of 68 references.

References · 57

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50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 8.

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  1. 2025PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Renderingcited by 41
  2. 2025BannerAgency: Advertising Banner Design with Multimodal LLM Agentscited by 11
  3. 2025Fewer Denoising Steps or Cheaper Per-Step Inference: Towards Compute-Optimal Diffusion Model Deploymentcited by 4
  4. 2026One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generationcited by 4
  5. 2025Emotion-Director: Bridging Affective Shortcut in Emotion-Oriented Image Generationcited by 3
  6. 2026Bridging Human Evaluation to Infrared and Visible Image Fusioncited by 3
  7. 2026simpleposter: A simple baseline for product poster generationcited by 3
  8. 2025Teleportraits: Training-Free People Insertion into Any Scenecited by 2
  9. 2025TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generationcited by 1
  10. 2026Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generationcited by 1
  11. 2026Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Modelscited by 1
  12. 2025Attacks on Approximate Caches in Text-to-Image Diffusion Modelscited by 0
3 more
  1. 2026Multi-Object Advertisement Creative Generationcited by 0
  2. 2026KD-CVG: A Knowledge-Driven Approach for Creative Video Generationcited by 0
  3. 2026CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learningcited by 0

Similar, not cited

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  1. 2024Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
  2. 2025CTR-Driven Advertising Image Generation with Multimodal Large Language Models
  3. 2026Generative Refinement Networks for Visual Synthesis
  4. 2025ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models
  5. 2025Personalized Image Generation for Recommendations Beyond Catalogs
  6. 2026One Pass Is Not Enough: Recursive Latent Refinement for Generative Models
  7. 2026Personalized Image Generation via Human-in-the-loop Bayesian Optimization
  8. 2023AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting