3 papers
cs.CV2025
Modular Prompt Learning Improves Vision-Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Pre-trained vision-language models are able to interpret visual concepts and language semantics. Prompt learning, a method of constructing prompts for text encoders or image encode…
cs.LG2024
Differentiable Prompt Learning for Vision Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Prompt learning is an effective way to exploit the potential of large-scale pre-trained foundational models. Continuous prompts parameterize context tokens in prompts by turning th…
cs.LG2021
Network Graph Based Neural Architecture Search
Zhenhan Huang, Chunheng Jiang, Pin-Yu Chen +1
Neural architecture search enables automation of architecture design. Despite its success, it is computationally costly and does not provide an insight on how to design a desirable…