6 papers
Addressing Missing and Noisy Modalities in One Solution: Unified Modality-Quality Framework for Low-quality Multimodal Data
Sijie Mai, Shiqin Han, Haifeng Hu
Multimodal data encountered in real-world scenarios are typically of low quality, with noisy modalities and missing modalities being typical forms that severely hinder model perfor…
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
As a knowledge discovery task over heterogeneous data sources, current Multimodal Affective Computing (MAC) heavily rely on the completeness of multiple modalities to accurately un…
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…
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis
Sijie Mai, Yu Zhao, Ying Zeng +2
Multimodal sentiment analysis aims to effectively integrate information from various sources to infer sentiment, where in many cases there are no annotations for unimodal labels. T…
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…