63 citations · 65 across the 11 of their papers we have counts for
13 papers
MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition
Pengfei Shao, Jisheng Dang, Jiawen Fang +7
Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic, and visual modalities. Alth…
Read-Best Is Not Steer-Best: A Probing--Steering Layer Dissociation in Omni-Modal Large Language Models
Yibo Wang, Jisheng Dang, Bimei Wang +7
Omni-modal large language models integrate text, audio, and image signals into a shared residual stream, where concepts such as emotion can be linearly decoded and causally modifie…
AffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related Scenes
Yibo Wang, Rui Yang, Jisheng Dang +7
Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding, yet affective reasoning remains vulnerable to shortcut behavior. Models may pred…
AlignFace: Human-Aligned Face Similarity Metric with Interpretable Concept Relations
Ying Huang, Wencan Zhang, Brian Y. Lim
Computer vision models for generated facial content, such as face editing and privacy protection, increasingly affect people, requiring similarity metrics that serve as faithful pr…
SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketches
Wencan Zhang, Mario Michelessa, Xuejun Zhao +1
Saliency map visualizations explain image-based AI predictions by pointing to regions, but these are often unintuitive and semantically unclear, leaving an interpretability gap. We…
MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning
Shang Ma, Jisheng Dang, Wencan Zhang +6
We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intelligence reasoning. A key fea…
On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR
Hao Ye, Jisheng Dang, Junfeng Fang +7
Recent extensive research has demonstrated that the enhanced reasoning capabilities acquired by models through Reinforcement Learning with Verifiable Rewards (RLVR) are primarily c…
i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
Haoyang Chen, Peiyan Sun, Qiyuan Song +5
Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate…
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning
Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim
Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when expla…
Towards Relatable Explainable AI with the Perceptual Process
Wencan Zhang, Brian Y. Lim
Machine learning models need to provide contrastive explanations, since people often seek to understand why a puzzling prediction occurred instead of some expected outcome. Current…
Exploiting Explanations for Model Inversion Attacks
Xuejun Zhao, Wencan Zhang, Xiaokui Xiao +1
The successful deployment of artificial intelligence (AI) in many domains from healthcare to hiring requires their responsible use, particularly in model explanations and privacy.…
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations
Danding Wang, Wencan Zhang, Brian Y. Lim
Feature attribution is widely used in interpretable machine learning to explain how influential each measured input feature value is for an output inference. However, measurements…
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim
Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when expla…