4 citations · 13 across the 9 of their papers we have counts for
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cs.CV2024★ 1 cited
Advancing Textual Prompt Learning with Anchored Attributes
Zheng Li, Yibing Song, Ming-Ming Cheng +2
Textual-based prompt learning methods primarily employ multiple learnable soft prompts and hard class tokens in a cascading manner as text inputs, aiming to align image and text (c…
cs.CV2024
Cascade Prompt Learning for Vision-Language Model Adaptation
Ge Wu, Xin Zhang, Zheng Li +4
Prompt learning has surfaced as an effective approach to enhance the performance of Vision-Language Models (VLMs) like CLIP when applied to downstream tasks. However, current learn…
cs.CV2024★ 2 cited
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
Zheng Li, Xiang Li, Xinyi Fu +4
Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses o…