3 citations · 4 across the 6 of their papers we have counts for
7 papers
PolyOCR-Venus: Unified OCR Foundation Models for Text-Centric Visual Intelligence
GuangJian Team, Kaili Huang, Yongshuo Zhang +22
Optical Character Recognition (OCR) is evolving from plain-text transcription toward general visual intelligence, requiring models to recognize, localize, and reason over textual i…
A Brief Overview: On-Policy Self-Distillation In Large Language Models
Fangming Cui, Sunan Li, Jiahong Li
On-Policy Self-Distillation (OPSD) is a unified learning framework in which a single large language model acts simultaneously as both teacher and student. Unlike conventional knowl…
Rethinking Agentic Reinforcement Learning In Large Language Models
Fangming Cui, Ruixiao Zhu, Cheng Fang +2
Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined environments. However, the adve…
Enhancing Target-unspecific Tasks through a Features Matrix
Fangming Cui, Yonggang Zhang, Xuan Wang +2
Recent developments in prompt learning of large Vision-Language Models (VLMs) have significantly improved performance in target-specific tasks. However, these prompting methods oft…
Generalizable Prompt Learning of CLIP: A Brief Overview
Fangming Cui, Yonggang Zhang, Xuan Wang +2
Existing vision-language models (VLMs) such as CLIP have showcased an impressive capability to generalize well across various downstream tasks. These models leverage the synergy be…
A Similarity Paradigm Through Textual Regularization Without Forgetting
Fangming Cui, Jan Fong, Rongfei Zeng +2
Prompt learning has emerged as a promising method for adapting pre-trained visual-language models (VLMs) to a range of downstream tasks. While optimizing the context can be effecti…
Advancing Prompt Learning through an External Layer
Fangming Cui, Xun Yang, Chao Wu +2
Prompt learning represents a promising method for adapting pre-trained vision-language models (VLMs) to various downstream tasks by learning a set of text embeddings. One challenge…