3 papers
cs.CV2026
U-shaped Multi-granularity Learning for Vision-Language Models
Biao Chen, Yunqian Yu, Xiangxu Zhao +3
The prompt learning paradigm for vision-language models is effective yet faces a granularity dilemma: global prompts lack fine-grained semantic awareness, while local prompts ignor…
cs.CV2025
Dropout Prompt Learning: Towards Robust and Adaptive Vision-Language Models
Biao Chen, Lin Zuo, Mengmeng Jing +2
Dropout is a widely used regularization technique which improves the generalization ability of a model by randomly dropping neurons. In light of this, we propose Dropout Prompt Lea…
cs.NE2024
Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
Lin Zuo, Yongqi Ding, Mengmeng Jing +3
This paper explores the application of spiking neural networks (SNNs), known for their low-power binary spikes, to bearing fault diagnosis, bridging the gap between high-performanc…