AMCCL: Adaptive Multi-scale Convolution Fusion Network with Contrastive Learning for Multimodal Sentiment Analysis

Abstract: Multimodal Sentiment Analysis (MSA) requires robust representations that capture both cross-modal consistency and intra-modal distinctions. Existing fusion methods often fail to adapt to diverse sentiment cues and neglect inter-modal correlations, while contrastive learning approaches insufficiently consider pair distribution and loss design. We propose an Adaptive Multi-scale Convolution fusion network with Contrastive Learning for multimodal sentiment analysis (AMCCL), which dynamically fuses multimodal information using an Adaptive Multi-scale Convolution (AMC) module. The AMC module dynamically fuses features through multi-scale convolutions with adaptive weighting and squeeze-and-excitation block to enhance salient channels. Our fine-grained contrastive learning leverages sentiment polarity and intensity, with tailored loss functions to strengthen the positive pairs and balance the inter-modal and intra-modal relations. Extensive evaluations on the MOSI and MOSEI datasets confirm that AMCCL delivers superior performance relative to state-of-the-art approaches.

@inproceedings{yu2026amccl,
  title={AMCCL: Adaptive Multi-scale Convolution Fusion Network with Contrastive Learning for Multimodal Sentiment Analysis},
  author={Yu, Jiakang and Li, Mingxin and Deng, Hongtao and Gao, Wang and Zhu, Xun},
  booktitle={Pacific Rim International Conference on Artificial Intelligence},
  pages={505--512},
  year={2026}
}

Jiakang Yu (余家康)