3 citations · 8 across the 8 of their papers we have counts for
8 papers
Towards Robust and Accurate Visual Prompting
Qi Li, Liangzhi Li, Zhouqiang Jiang +1
Visual prompting, an efficient method for transfer learning, has shown its potential in vision tasks. However, previous works focus exclusively on VP from standard source models, i…
Instruct Me More! Random Prompting for Visual In-Context Learning
Jiahao Zhang, Bowen Wang, Liangzhi Li +2
Large-scale models trained on extensive datasets, have emerged as the preferred approach due to their high generalizability across various tasks. In-context learning (ICL), a popul…
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
Guoxin Chen, Yiming Qian, Bowen Wang +1
The large language models have achieved superior performance on various natural language tasks. One major drawback of such approaches is they are resource-intensive in fine-tuning…
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction
Junyi Liu, Liangzhi Li, Tong Xiang +2
Since ChatGPT released its API for public use, the number of applications built on top of commercial large language models (LLMs) increase exponentially. One popular usage of such…
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Aohan Zeng, Mingdao Liu, Rui Lu +4
Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models s…
Improving Facade Parsing with Vision Transformers and Line Integration
Bowen Wang, Jiaxing Zhang, Ran Zhang +3
Facade parsing stands as a pivotal computer vision task with far-reaching applications in areas like architecture, urban planning, and energy efficiency. Despite the recent success…