9 citations · 25 across the 16 of their papers we have counts for
16 papers
Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding
Yun Li, Zhe Liu, Yajing Kong +6
Applying Multimodal Large Language Models (MLLMs) to video understanding presents significant challenges due to the need to model temporal relations across frames. Existing approac…
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training
Yun Li, Zhe Liu, Lina Yao
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen combinations of seen attributes and objects. Current CLIP-based methods in CZSL, despite their advancements, often…
AI Safety in Generative AI Large Language Models: A Survey
Jaymari Chua, Yun Li, Shiyi Yang +2
Large Language Model (LLMs) such as ChatGPT that exhibit generative AI capabilities are facing accelerated adoption and innovation. The increased presence of Generative AI (GAI) in…
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning
Yun Li, Zhe Liu, Hang Chen +1
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies, despite their advan…
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning
Yun Li, Zhe Liu, Saurav Jha +2
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world…
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning
Zhe Liu, Yun Li, Lina Yao +4
The task of Compositional Zero-Shot Learning (CZSL) is to recognize images of novel state-object compositions that are absent during the training stage. Previous methods of learnin…
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus
Yun Li, Zhe Liu, Lina Yao +3
With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in E…
Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis
Yun Li, Zhe Liu, Lina Yao +2
EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are si…
Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
Alvaro Gomariz, Huanxiang Lu, Yun Yvonna Li +7
Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been succ…
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning
Yun Li, Zhe Liu, Xiaojun Chang +2
Zero-Shot Learning (ZSL) aims to transfer classification capability from seen to unseen classes. Recent methods have proved that generalization and specialization are two essential…
Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning
Zhe Liu, Yun Li, Lina Yao +2
Current approaches to Zero-Shot Learning (ZSL) struggle to learn generalizable semantic knowledge capable of capturing complex correlations. Inspired by \emph{Spiral Curriculum}, w…
An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning
Yun Li, Zhe Liu, Lina Yao +3
Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations. A promising strategy is to learn a global-local repre…
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification
Yun Li, Zhe Liu, Lina Yao +1
Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to semantically related unseen classes, which are absent during training. The promising strategies for ZSL are…
Task Aligned Generative Meta-learning for Zero-shot Learning
Zhe Liu, Yun Li, Lina Yao +2
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer…
Generative Adversarial U-Net for Domain-free Medical Image Augmentation
Xiaocong Chen, Yun Li, Lina Yao +2
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. Without a sufficient number of training samples, deep learning ba…
Agglomerative Neural Networks for Multi-view Clustering
Zhe Liu, Yun Li, Lina Yao +2
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, the pairwise compariso…