2 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
Yeyuan Wang, Dehong Gao, Bin Li +7
The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks.…
Enhancing Fine-Grained Vision-Language Pretraining with Negative Augmented Samples
Yeyuan Wang, Dehong Gao, Lei Yi +4
Existing Vision-Language Pretraining (VLP) methods have achieved remarkable improvements across a variety of vision-language tasks, confirming their effectiveness in capturing coar…
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning
Yufei Ma, Zihan Liang, Huangyu Dai +9
The growing demand for larger-scale models in the development of \textbf{L}arge \textbf{L}anguage \textbf{M}odels (LLMs) poses challenges for efficient training within limited comp…
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
Zhiming Yang, Haining Gao, Dehong Gao +5
Click-through rate (CTR) prediction is one of the fundamental tasks in the industry, especially in e-commerce, social media, and streaming media. It directly impacts website revenu…
General2Specialized LLMs Translation for E-commerce
Kaidi Chen, Ben Chen, Dehong Gao +6
Existing Neural Machine Translation (NMT) models mainly handle translation in the general domain, while overlooking domains with special writing formulas, such as e-commerce and le…