40 citations · 69 across the 12 of their papers we have counts for
12 papers
EmoLLM: Appraisal-Grounded Cognitive-Emotional Co-Reasoning in Large Language Models
Yifei Zhang, Mingyang Li, Henry Gao +1
Large language models (LLMs) demonstrate strong cognitive intelligence (IQ), yet many real-world interactions also require emotional intelligence (EQ) to produce responses that are…
Cross-modal RAG: Sub-dimensional Text-to-Image Retrieval-Augmented Generation
Mengdan Zhu, Senhao Cheng, Guangji Bai +2
Text-to-image generation increasingly demands access to domain-specific, fine-grained, and rapidly evolving knowledge that pretrained models cannot fully capture, necessitating the…
MEGL: Multimodal Explanation-Guided Learning
Yifei Zhang, Tianxu Jiang, Bo Pan +3
Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for addressing their "black box" nature, particularly in tasks like image classification.…
GraphNarrator: Generating Textual Explanations for Graph Neural Networks
Bo Pan, Zhen Xiong, Guanchen Wu +3
Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despi…
Explaining latent representations of generative models with large multimodal models
Mengdan Zhu, Zhenke Liu, Bo Pan +2
Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence. With the rise of the large multimodal…
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
Guangji Bai, Zheng Chai, Chen Ling +11
The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated models like OpenAI's ChatGPT, represents a significant advancement in artificial intelligence. Th…
SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation
Bo Pan, Zhenke Liu, Yifei Zhang +1
Explainable AI seeks to bring light to the decision-making processes of black-box models. Traditional saliency-based methods, while highlighting influential data segments, often la…
Controllable Data Generation Via Iterative Data-Property Mutual Mappings
Bo Pan, Muran Qin, Shiyu Wang +2
Deep generative models have been widely used for their ability to generate realistic data samples in various areas, such as images, molecules, text, and speech. One major goal of d…
Saliency-Guided Hidden Associative Replay for Continual Learning
Guangji Bai, Qilong Zhao, Xiaoyang Jiang +2
Continual Learning is a burgeoning domain in next-generation AI, focusing on training neural networks over a sequence of tasks akin to human learning. While CL provides an edge ove…
Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation
Xiangru Li, Yifei Zhang, Liang Zhao
The Segment Anything Model (SAM) is a powerful foundation model that introduced revolutionary advancements in natural image segmentation. However, its performance remains sub-optim…
Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations
Yifei Zhang, James Song, Siyi Gu +4
Explainable AI (XAI) has gained significant attention for providing insights into the decision-making processes of deep learning models, particularly for image classification tasks…
Visual Attention Prompted Prediction and Learning
Yifei Zhang, Siyi Gu, Bo Pan +4
Visual explanation (attention)-guided learning uses not only labels but also explanations to guide model reasoning process. While visual attention-guided learning has shown promisi…