11 papers
Adaptive Hierarchical Representation Alliance for Multimodal Learning
Chunlei Meng, Pengbin Feng, Jacqueline J. Pang +5
Multimodal models often align language, vision, and audio in a single final-layer latent space, implicitly assuming that task-relevant evidence emerges at the same semantic depth a…
Rethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations
Chunlei Meng, Jacqueline J. Pang, Pengbin Feng +3
Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete. Existing methods for inco…
Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability
Yi Liu, Hongda Zhang, Leyao Zou +7
Path planning under partial observability remains challenging because an agent must make long-horizon navigation decisions from only locally bounded observations. Nevertheless, his…
Group Cognition Learning: Making Everything Better Through Governed Two-Stage Agents Collaboration
Chunlei Meng, Pengbin Feng, Rong Fu +7
Centralized multimodal learning commonly compresses language, acoustic, and visual signals into a single fused representation for prediction. While effective, this paradigm suffers…
Mitigating Shared-Private Branch Imbalance via Dual-Branch Rebalancing for Multimodal Sentiment Analysis
Chunlei Meng, Jiabin Luo, Pengbin Feng +4
Multimodal Sentiment Analysis (MSA) requires integrating language, acoustic, and visual signals without sacrificing modality-specific sentiment evidence. Existing methods mainly im…
CLCR: Cross-Level Semantic Collaborative Representation for Multimodal Learning
Chunlei Meng, Guanhong Huang, Rong Fu +3
Multimodal learning aims to capture both shared and private information from multiple modalities. However, existing methods that project all modalities into a single latent space f…