activity
20162024
most citedLISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model

4 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL20242 cited

Scalable Language Model with Generalized Continual Learning

Bohao Peng, Zhuotao Tian, Shu Liu +2

Continual learning has gained increasing importance as it facilitates the acquisition and refinement of scalable knowledge and skills in language models. However, existing methods…

cs.CV20244 cited

LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model

Senqiao Yang, Tianyuan Qu, Xin Lai +4

While LISA effectively bridges the gap between segmentation and large language models to enable reasoning segmentation, it poses certain limitations: unable to distinguish differen…

cs.CV2024

MOODv2: Masked Image Modeling for Out-of-Distribution Detection

Jingyao Li, Pengguang Chen, Shaozuo Yu +2

The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples. While previous methods predomin…

cs.SI20231 cited

HyperS2V: A Framework for Structural Representation of Nodes in Hyper Networks

Shu Liu, Cameron Lai, Fujio Toriumi

In contrast to regular (simple) networks, hyper networks possess the ability to depict more complex relationships among nodes and store extensive information. Such networks are com…

cs.CV2023

Learning Context-aware Classifier for Semantic Segmentation

Zhuotao Tian, Jiequan Cui, Li Jiang +5

Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature dist…

cs.CV20161 cited

Label distribution based facial attractiveness computation by deep residual learning

Shu Liu, Bo Li, Yangyu Fan +2

Two challenges lie in the facial attractiveness computation research: the lack of true attractiveness labels (scores), and the lack of an accurate face representation. In order to…