Distance-aware Soft Prompt Guidance for Multimodal Valence-Arousal Estimation
Valence-arousal (VA) estimation is crucial for capturing the nuanced nature of human emotions in naturalistic environments. While pre-trained vision-language models such as CLIP have demonstrated remarkable semantic alignment capabilities, their application to continuous regression tasks is often limited by the discrete nature of text prompts. In this paper, we propose a novel multimodal framework for VA estimation that introduces Distance-aware Soft Prompt Guidance to bridge the gap between semantic representations and continuous affective dimensions. Specifically, we partition the VA space into multiple discrete regions, each associated with distinct textual descriptions. Rather than re...
| Authors | Byeongjin Jung, Chanyeong Park, Sejoon Lim |
|---|---|
| Primary category | cs.CV |
| Published | 12 March 2026 |
| Last updated at source | 31 July 2026 |
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