24 citations · 33 across the 17 of their papers we have counts for
4 papers · 1 filter
What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies
Duo Li, Haojun Pan, Zuhao Yang +4
Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating mismatched ones in a shared embed…
CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learning
Yajiao Xu, Jin Zhang, Jiangbo Ai +4
High-quality e-commerce creatives are essential for presenting products and conveying marketing messages. Recent diffusion models enable scalable creative generation and produce vi…
OmniPack: Unified Token Compression for Efficient Omni-modal Large Language Models
Wanshun Su, Yang Shi, Feihu Liu +10
Omni-modal large language models (Omni-LLMs) have achieved remarkable performance on audio-visual understanding tasks, but processing long and highly redundant visual and audio tok…
IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Haonan Qi, Jin Cao, Yongqi Zhang +9
Industrial products such as valves and circuit breakers are defined by dense technical specifications that govern procurement, compatibility, and safety across supply chains. These…