A Conflict-Aware Penalty and Statistical Loss Framework for Balancing Modalities and Enhancing Stability in Multimodal Sentiment Analysis
Jianheng Dai, Jiazhang Liang, Sijie Mai
Why It Matters
What makes this one worth your time
This work is significant for researchers and engineers in sentiment analysis and multimodal systems, as it proposes a method to effectively balance different modalities, potentially leading to more robust models.
A novel framework to enhance stability in multimodal sentiment analysis by addressing gradient conflicts.
Summary
The paper introduces a Conflict-aware Penalty and Statistical Loss framework to improve stability and balance among modalities in Multimodal Sentiment Analysis, addressing the dominance of text encoders over acoustic and visual modalities during training.
Key contributions
- Introduction of the Conflict-aware Penalty to mitigate gradient conflicts.
- Development of Statistical Loss for aligning predicted and empirical statistics.
- Implementation of a unified framework that includes adaptive modality encoding and gated cross-modal fusion.
Notable insights
- The Conflict-aware Penalty specifically targets gradient norm conflicts, which is a nuanced approach to improving training stability.
- The integration of Statistical Loss to align predicted and empirical statistics suggests a sophisticated understanding of distributional properties in multimodal data.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.28575v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) fuses text, acoustic, and visual streams to infer sentiment. Because pre-trained text encoders are far more expressive than their acoustic and visual counterparts, the text modality tends to dominate optimization, suppressing weaker modalities and inducing gradient norm conflicts that destabilize training. To address this, we propose a Conflict-aware Penalty (CP) that detects and penalizes gradient norm conflicts at each training step, and a Statistical Loss (SL) that aligns predicted distribution statistics with empirical input statistics. Crucially, CP prevents dominant modality gradients from interfering with the SL objective, enabling synergistic training within a unified framework incorporating adaptive modality encoding, gated cross-modal fusion, and unimodal auxiliary heads. Experiments on CMU-MOSI demonstrate state-of-the-art performance, with ablation studies confirming the effectiveness of each component.