8 citations · 24 across the 15 of their papers we have counts for
15 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…
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…
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…
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…
Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding
Zhe Liu, Xianzhi Wang, Lina Yao +3
Predicting consumers' purchasing behaviors is critical for targeted advertisement and sales promotion in e-commerce. Human faces are an invaluable source of information for gaining…
Spectrum-Guided Adversarial Disparity Learning
Zhe Liu, Lina Yao, Lei Bai +2
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between…
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…
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
Zhe Liu, Lina Yao, Xianzhi Wang +2
Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models. Given the effectiveness of generative adversarial learn…
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Xiang Zhang, Lina Yao, Manqing Dong +3
Objective: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the world's population. Most of t…