E-commerce videos are information-dense and frequently compared by consumers evaluating products and merchants assessing marketing strategies. However, existing multimodal models mainly focus on single-video understanding and have limited ability to compare information across videos. We introduce AdsCVR, the first e-co...
Jing-Han Zhao, Yi-Man Hu, Liang Wu et al.· 0 citations
Pailitao-MMSearch is presented, one native e-commerce multimodal search foundation model designed to bridge this gap, and introduces three key innovations:HybSID (Hybrid Semantic ID);(2)a two-stage continual pre-training strategy; and (3)a hybrid reasoning post-training pipeline.
Xiaohan Ye, Xu Chen, Zi-Han Gong et al.· arXiv.org· 0 citations
SAGE introduces a parameter-efficient multi-modal alignment framework for constrained auto-bidding with Large Language Models, demonstrating that SAGE consistently achieves superior performance while tuning less than 10% of the trainable parameters required by full fine-tuning.
TransRetrieval is presented, a Transformer-based retrieval framework that scales with both computational budget and cross-domain data and introduces weighted average aggregation, which restores the homogeneous-token assumption Transformers rely on, and target token compression that cuts per-candidate FLOPs while preser...
Zhi-Fei Zheng, Yun-Fei Liu, Bin Liu et al.· 0 citations
This paper designs a target-focused perception mechanism that identifies and enhances visual tokens of the search target, consisting of a target Enhancer and a learning strategy based on on-policy distillation and attention guidance loss, enabling the model to focus on search-target regions.
Xiaohan Fan, Yueran Liu, Shengyu Zhou et al.· 0 citations
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