22 citations · 22 across the 6 of their papers we have counts for
8 papers
PACE-MoE: Adaptive Domain Sampling and Domain-Specific LoRA for Medical Visual Grounding
Zhenxuan Wang, Jisheng Dang, Ronghao Lin +6
Medical visual grounding requires models to align clinical descriptions with spatial evidence across heterogeneous imaging domains. However, existing multi-domain post-training str…
Seeing What Should Be Heard: Diagnosing and Repairing Cross-Modal Shortcuts in Omni-Modal LLMs
Yueran Ma, Ronghao Lin
Omni-modal large language models (LLMs) are expected to answer a question using the modality it explicitly refers to. However, existing training paradigms rarely verify whether mod…
CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning
Ronghao Lin, Qiaolin He, Sijie Mai +5
Multimodal machine learning, mimicking the human brain's ability to integrate various modalities has seen rapid growth. Most previous multimodal models are trained on perfectly pai…
MissMAC-Bench: Building Solid Benchmark for Missing Modality Issue in Robust Multimodal Affective Computing
Ronghao Lin, Honghao Lu, Ruixing Wu +5
Current Multimodal Affective Computing (MAC) systems heavily rely on the completeness of multiple modalities to accurately understand human's affective state. However, in real-worl…
E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model
Ronghao Lin, Shuai Shen, Weipeng Hu +5
Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved te…
Multi-source Multimodal Progressive Domain Adaption for Audio-Visual Deception Detection
Ronghao Lin, Sijie Mai, Ying Zeng +3
This paper presents the winning approach for the 1st MultiModal Deception Detection (MMDD) Challenge at the 1st Workshop on Subtle Visual Computing (SVC). Aiming at the domain shif…
End-to-end Semantic-centric Video-based Multimodal Affective Computing
Ronghao Lin, Ying Zeng, Sijie Mai +1
In the pathway toward Artificial General Intelligence (AGI), understanding human's affection is essential to enhance machine's cognition abilities. For achieving more sensual human…
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis
Ronghao Lin, Haifeng Hu
Multimodal representation learning is a challenging task in which previous work mostly focus on either uni-modality pre-training or cross-modality fusion. In fact, we regard modeli…