3 papers
cs.CL2025
Semi-Supervised Synthetic Data Generation with Fine-Grained Relevance Control for Short Video Search Relevance Modeling
Haoran Li, Zhiming Su, Junyan Yao +6
Synthetic data is widely adopted in embedding models to ensure diversity in training data distributions across dimensions such as difficulty, length, and language. However, existin…
cs.AI2024
MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
Tao Chen, Enwei Zhang, Yuting Gao +5
Although In-Context Learning (ICL) brings remarkable performance gains to Large Language Models (LLMs), the improvements remain lower than fine-tuning on downstream tasks. This pap…
cs.CV2024
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Wensheng Pan, Timin Gao, Yan Zhang +10
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging seman…