KwaiMind is a commercial image editing system that combines general editing capabilities with e-commerce specialization. It uses an agent-based data engine with 1.8 million editing pairs and a multimodal diffusion transformer trained through pre‑training, fine‑tuning, preference optimization, and online reinforcement learning. The system is guided by a vision‑language judge and specialized rewards for click‑through rate, text rendering, and product consistency, and it achieves top scores on ImgEdit, GEdit, REDEdit, and the new Ecom‑Bench, while improving predicted and actual CTR in offline and online experiments.
By Junlong Wu, Zijun Li, Yuting Hu, Jia Sun, Pengcheng Wei, Yimin Zhou, Honglie Wang, Huaiqing Wang, Dewen Fan, Fei Zuo, Haixuan Gao, Lihui Peng, Tingxuan She, Yuqing Li, Boheng Zhang, Fan Yang, Wenwu Ou
arXiv:2608. 00584v1 Announce Type: cross Abstract: Recent advances in image generation and editing have made prompt quality a key bottleneck for e-commerce creatives.
By Jingtong Chen, Jiahui Wang, Xue Zhao, ShaoGuo Liu, Minghao Li
PinDCO is a scalable dynamic creative optimization system designed for Pinterest’s billion‑scale visual discovery platform. It uses a Creative Component Fusion Network to score ad creatives by modeling individual components (image, title, layout) with dedicated towers and fusing their representations, while a Pixel‑aware Adjustment Module tailors scores to creative size for better whole‑page outcomes. The system incorporates a lightweight pre‑selection model, caching, and dynamic batching to handle large candidate volumes, achieving a 3.09% lift in ad click‑through rate in online experiments.
By Yu Hao, Yuchun Li, Peimeng Sui, Meilin Liu, Tianyuan Cui, Hao Li, Zicong Zhou, Akanksha Baid
arXiv:2608. 04504v1 Announce Type: cross Abstract: Vision-language models excel in many multimodal tasks but remain prone to a subtle yet impactful failure mode: they tend to overestimate dominant visual-textual cues while underestimating sparse but decision-critical contextual variables.
By Shuo Liu, Huixiang Cai, Weiru Zhang, Xiaoyi Zeng
Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks the inherently dynamic nature of human aesthetic perception, which relies on subconscious comparison against implicit visual references.
CommerceVibe is a system that generates e‑commerce creatives by synthesizing executable HTML/CSS code conditioned on product images, design requirements, and product information. It uses dual‑feedback reinforcement learning, combining rule‑based checks for text readability, product visibility, and layout validity with visual feedback from a vision‑language model that evaluates perceptual and commercial aspects. After fine‑tuning a large language model on 28,000 examples and applying dual‑feedback reinforcement learning, CommerceVibe achieves a weighted score of 94.0/100 on a 1,300‑case benchmark, outperforming both its SFT‑only counterpart and external models, and is validated by expert blind evaluations.
By Yajiao Xu, Jin Zhang, Jiangbo Ai, Tao Jiang, Mo Xu, Lina Huang, Chengfu Huo