4 citations · 13 across the 9 of their papers we have counts for
9 papers
AnchorOPT: Towards Optimizing Dynamic Anchors for Adaptive Prompt Learning
Zheng Li, Yibing Song, Xin Zhang +3
Existing prompt learning methods, which are built upon CLIP models, leverage textual tokens as anchors to guide the learnable soft tokens. This guidance improves CLIP generalizatio…
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…
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…
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…
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?
Zheng Li, Yuxuan Li, Penghai Zhao +3
Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic…
Curriculum Temperature for Knowledge Distillation
Zheng Li, Xiang Li, Lingfeng Yang +5
Most existing distillation methods ignore the flexible role of the temperature in the loss function and fix it as a hyper-parameter that can be decided by an inefficient grid searc…
Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation
Zheng Li, Xiang Li, Lingfeng Yang +2
Knowledge distillation usually transfers the knowledge from a pre-trained cumbersome teacher network to a compact student network, which follows the classical teacher-teaching-stud…
Online Knowledge Distillation for Efficient Pose Estimation
Zheng Li, Jingwen Ye, Mingli Song +2
Existing state-of-the-art human pose estimation methods require heavy computational resources for accurate predictions. One promising technique to obtain an accurate yet lightweigh…
Online Knowledge Distillation via Multi-branch Diversity Enhancement
Zheng Li, Ying Huang, Defang Chen +3
Knowledge distillation is an effective method to transfer the knowledge from the cumbersome teacher model to the lightweight student model. Online knowledge distillation uses the e…