# Temporal-Spatial Decouple before Act: Disentangled Representation Learning for Multimodal Sentiment Analysis

arXiv: https://arxiv.org/abs/2601.13659 · HTML: https://mlsanity.com/paper/2601.13659/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Efficient Low-rank Multimodal Fusion with Modality-Specific Factors](https://mlsanity.com/paper/1806.00064/) (2018)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [Decoupled Multimodal Distilling for Emotion Recognition](https://mlsanity.com/paper/2303.13802/) (2023)
- [Classifier-guided Gradient Modulation for Enhanced Multimodal Learning](https://mlsanity.com/paper/2411.01409/) (2024)
- [Towards Multimodal Sentiment Analysis Debiasing via Bias Purification](https://mlsanity.com/paper/2403.05023/) (2024)
- [DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis](https://mlsanity.com/paper/2412.12225/) (2024)
- [Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content](https://mlsanity.com/paper/2412.10460/) (2024)
- [SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation](https://mlsanity.com/paper/2312.17071/) (2023)
- [Joint Multimodal Transformer for Emotion Recognition in the Wild](https://mlsanity.com/paper/2403.10488/) (2024)
- [Fine-grained Disentangled Representation Learning for Multimodal Emotion Recognition](https://mlsanity.com/paper/2312.13567/) (2023)
- [Semi-IIN: Semi-supervised Intra-inter modal Interaction Learning Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2412.09784/) (2024)
- [CTA-Net: A CNN-Transformer Aggregation Network for Improving Multi-Scale Feature Extraction](https://mlsanity.com/paper/2410.11428/) (2024)

## Built on by (most cited first, 3 of 3)

- [Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2602.19585/) (2026)
- [CLCR: Cross-Level Semantic Collaborative Representation for Multimodal Learning](https://mlsanity.com/paper/2602.19605/) (2026)
- [Group Cognition Learning: Making Everything Better Through Governed Two-Stage Agents Collaboration](https://mlsanity.com/paper/2605.00370/) (2026)

## Abstract (arXiv)

Multimodal Sentiment Analysis integrates Linguistic, Visual, and Acoustic. Mainstream approaches based on modality-invariant and modality-specific factorization or on complex fusion still rely on spatiotemporal mixed modeling. This ignores spatiotemporal heterogeneity, leading to spatiotemporal information asymmetry and thus limited performance. Hence, we propose TSDA, Temporal-Spatial Decouple before Act, which explicitly decouples each modality into temporal dynamics and spatial structural context before any interaction. For every modality, a temporal encoder and a spatial encoder project signals into separate temporal and spatial body. Factor-Consistent Cross-Modal Alignment then aligns temporal features only with their temporal counterparts across modalities, and spatial features only with their spatial counterparts. Factor specific supervision and decorrelation regularization reduce cross factor leakage while preserving complementarity. A Gated Recouple module subsequently recouples the aligned streams for task. Extensive experiments show that TSDA outperforms baselines. Ablation analysis studies confirm the necessity and interpretability of the design.