Vision-Guided Text Prompt Tuning for Multimodal Sentiment Analysis
Multimodal sentiment analysis requires effective modeling of both verbal semantics and non-verbal affective cues. A central challenge is to calibrate text-centered sentiment understanding with visual facial evidence in a controlled, adaptive, and parameter-efficient manner. Text usually serves as the semantic anchor, whereas visual cues provide complementary evidence for ambiguous or implicit expressions; however, indiscriminate fusion may introduce visual noise and distort textual semantics. Moreover, fully fine-tuning large visual and textual encoders is costly and prone to overfitting on limited and scenario-dependent MSA benchmarks. To address these issues, we propose Vision-Guided Te...
| Authors | Xiaoran Kou, Jingyi Wu, Peng Sun et al. |
|---|---|
| Primary category | cs.MM |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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