Papers with SIMS
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality (2022.aacl-main)
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| Challenge: | Existing methods to address missing modalities often assume a particular modality is completely missing due to recording or transmission error. |
| Approach: | They propose a missing modality-based meta-sampling approach for multimodal sentiment analysis with missing modalities . they conduct experiments on IEMOCAP, SIMS and CMU-MOSI datasets . |
| Outcome: | The proposed method significantly improves on existing models with a mixture of missing modalities. |
CLGSI: A Multimodal Sentiment Analysis Framework based on Contrastive Learning Guided by Sentiment Intensity (2024.findings-naacl)
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| Challenge: | Recent studies have focused on contrastive learning, but lack detailed learning of the distribution of sample pairs with different sentiment intensity differences in the contrastive training representation space. |
| Approach: | They propose a framework for multimodal sentiment analysis based on contrastive learning guided by sentiment intensity (CLGSI) it selects positive and negative sample pairs based upon sentiment intensity differences and assigns corresponding weights accordingly. |
| Outcome: | The proposed framework extracts common features between different modalities and then uses them to predict sentiment intensity. |
Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation (2026.findings-acl)
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Boyun Zhang, Zequn Xie, Fangming Feng, Zihan Zhang, Yongbo He, Chuxin Wang, Sihang Cai, Tao Jin, Qifei Zhang
| Challenge: | Existing discriminative approaches suffer from "confident but wrong" failure mode, blindly adapting to OOD noise leading to error accumulation. |
| Approach: | They propose a TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). |
| Outcome: | The proposed framework reduces MAE from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%) it also reduces the MAE of the MOSI to SIMS shift and achieves an 11.18-point gain over the baseline. |