2 citations · 2 across the 3 of their papers we have counts for
5 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…