1 citations · 1 across the 3 of their papers we have counts for
6 papers
Synergistic Dual-Branch Adaptation for Multi-modal Generalized Category Discovery
Yuxun Qu, Minyu Zhou, Yongqiang Tang +2
Generalized Category Discovery (GCD) aims to classify old categories and discover new ones from unlabeled data. Recent multi-modal approaches introduce retrieved or synthesized tex…
GradMAP: Faster Layer Pruning with Gradient Metric and Projection Compensation
Hao Liu, Guangyan Li, Wensheng Zhang +1
Large Language Models (LLMs) exhibit strong reasoning abilities, but their high computational costs limit their practical deployment. Recent studies reveal significant redundancy i…
Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework
Jiasheng Xu, Mingda Li, Yongqiang Tang +2
Large Language Models (LLMs) have demonstrated strong capabilities in web search and reasoning. However, their dependence on static training corpora makes them prone to factual err…
MGAA: Multi-Granular Adaptive Allocation fof Low-Rank Compression of LLMs
Guangyan Li, Yongqiang Tang, Wensheng Zhang
The enormous parameter scale of large language models (LLMs) has made model compression a research hotspot, which aims to alleviate computational resource demands during deployment…
AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery
Yuxun Qu, Yongqiang Tang, Chenyang Zhang +1
Different from the traditional semi-supervised learning paradigm that is constrained by the close-world assumption, Generalized Category Discovery (GCD) presumes that the unlabeled…
LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models
Guangyan Li, Yongqiang Tang, Wensheng Zhang
Large language models (LLMs) show excellent performance in difficult tasks, but they often require massive memories and computational resources. How to reduce the parameter scale o…