From the 1 of 9 linked papers with an AI index.
9 papers
U-shaped Multi-granularity Learning for Vision-Language Models
Biao Chen, Yunqian Yu, Xiangxu Zhao +3
The paper introduces UPrompt, a U‑shaped multi‑granularity prompt learning framework that combines global and local prompts for vision‑language models, improving fine‑grained seman…
Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks
Yongqi Ding, Kunshan Yang, Linze Li +3
Although the temporal spike dynamics of spiking neural networks (SNNs) enable low-power temporal pattern capture capabilities, they also incur inherent inconsistencies that severel…
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…
Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers
Yongqi Ding, Lin Zuo, Mengmeng Jing +3
Brain-inspired spiking neural networks (SNNs) promise to be a low-power alternative to computationally intensive artificial neural networks (ANNs), although performance gaps persis…
SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptation
Zixi Wang, Xiangxu Zhao, Tonglan Xie +2
Domain shifts are critical issues that harm the performance of machine learning. Unsupervised Domain Adaptation (UDA) mitigates this issue but suffers when the domain shifts are st…
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…