Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis | ScienceToStartup | ScienceToStartup

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References (31)

[1]

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

2026Chunlei Meng, Ziyang Zhou et al.

[2]

Multi-grained teacher-student joint representation learning for surface defect classification

2025Chunlei Meng, Jiacheng Yang et al.

[3]

CF-ViT: Cross-Feature Vision Transformer for Improving Feature Learning on Tiny Datasets

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"Tri-Subspace Disentanglement framework improves multimodal sentiment analysis by enhancing cross-modal synergies and modality-specific cues."

Multimodal Sentiment Analysis•Score: 5•View PDF ↗

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Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis

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References (31)

Founder's Pitch

"Tri-Subspace Disentanglement framework improves multimodal sentiment analysis by enhancing cross-modal synergies and modality-specific cues."

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Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis

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References (31)

Founder's Pitch

"Tri-Subspace Disentanglement framework improves multimodal sentiment analysis by enhancing cross-modal synergies and modality-specific cues."

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