Frequency Restoration and Modality Enforcement towards Resisting-corruption Multimodal Sentiment Analysis

For Multimodal Sentiment Analysis (MSA), previous methods concentrate on designing sophisticated fusion strategies and performing representation learning across heterogeneous modalities, aiming to leverage multimodal signals to detect human sentiment. However, these approaches fail to address the long-standing issue of corrupted modal details in videos, which may be caused by the challenge of the excessive loss of emotionally relevant semantics resulted from the degradation of detailed information. In this work, we aim to improve the robustness capacity of resisting corruption in MSA, by introducing a Hierarchical Frequency Restoration and Adaptive Modality Enforcement (HFR-AME) approach. The HFR-AME progressively recovers blurred detailed cues in each modality while enhancing the discriminative power of modal representations. Specifically, to reconstruct distinct frequency band features, we propose to equip the HFR module with a key component called the Frequency Multimodal UNet (FM-UNet), so as to utilize complementary modal features as conditions. This meticulous restoration process, performed from low to high frequency, facilitates the comprehensive recovery of intricate details. Meanwhile, to adaptively integrate these diverse frequency features, we introduce the AME module to enhance the beneficial modal frequencies while suppressing irrelevant ones, with the goal of strengthening the restored modal representations. Extensive experiments show our HFR-AME outperforms state-of-the-art methods on the CMU-MOSI and CMU-MOSEI datasets, improving 7-class accuracy by 0.5% and 0.6%, respectively. Further analysis also confirms its cross-lingual generalization and competitive computational efficiency. Our code is made available at https://github.com/nianhua20/HFR-AME.