← 研踪 PaperTrailCVF · CVPR 2026:cvf:8fb7d6429d2f3b5c1ab08c2b0ee9da1f · 记录 #1
Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-02 00:03
摘要
Multimodal Sentiment Analysis (MSA) integrates language, visual, and acoustic modalities to infer human sentiment. Most existing methods either focus on globally shared representations or modality-specific features, while overlooking signals that are shared only by certain modality pairs. This limits the expressiveness and discriminative power of multimodal representations. To address this limitation, we propose a Tri-Subspace Disentanglement (TSD) framework that explicitly factorizes features into three complementary subspaces: a common subspace capturing global consistency, submodally-shared subspaces modeling pairwise cross-modal synergies, and private subspaces preserving modality-specific cues. To keep these subspaces pure and independent, we introduce a decoupling supervisor together with structured regularization losses. We further design a Subspace-Aware Cross-Attention (SACA) fusion module that adaptively models and integrates information from the three subspaces to obtain richer and more robust representations. Experiments on CMU-MOSI and CMU-MOSEI demonstrate that TSD achieves state-of-the-art performance across all key metrics, reaching 0.691 MAE on CMU-MOSI and 54.6% Acc-7 on CMU-MOSEI under the unaligned setting, and also transfers well to multimodal intent recognition tasks. Ablation studies confirm that tri-subspaces disentanglement and SACA jointly enhance the modeling of multi-granular cross-modal sentiment cues.
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记录 #1 · 2026 · 最新观测版本
Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) integrates language, visual, and acoustic modalities to infer human sentiment. Most existing methods either focus on globally shared representations or modality-specific features, while overlooking signals that are shared only by certain modality pairs. This limits the expressiveness and discriminative power of multimodal representations. To address this limitation, we propose a Tri-Subspace Disentanglement (TSD) framework that explicitly factorizes features into three complementary subspaces: a common subspace capturing global consistency, submodally-shared subspaces modeling pairwise cross-modal synergies, and private subspaces preserving modality-specific cues. To keep these subspaces pure and independent, we introduce a decoupling supervisor together with structured regularization losses. We further design a Subspace-Aware Cross-Attention (SACA) fusion module that adaptively models and integrates information from the three subspaces to obtain richer and more robust representations. Experiments on CMU-MOSI and CMU-MOSEI demonstrate that TSD achieves state-of-the-art performance across all key metrics, reaching 0.691 MAE on CMU-MOSI and 54.6% Acc-7 on CMU-MOSEI under the unaligned setting, and also transfers well to multimodal intent recognition tasks. Ablation studies confirm that tri-subspaces disentanglement and SACA jointly enhance the modeling of multi-granular cross-modal sentiment cues.
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