Learning emotional prompt features with multiple views for visual emotion analysis

Qinfu Xu, Yiwei Wei, Shaozu Yuan, Jie Wu, Leiquan Wang, Chunlei Wu

Abstract: Highlights•This work reveals a novel standpoint for visual emotion analysis via observing the semantic space changes and achieves an effective and stable emotional semantic learning process.•We design the Multi-Views Prompt Learning method (MVP) to capture the emotional cues, achieving a new state-of-the-art (SOTA) performance.•Our work provides an in-depth analysis and insights of the multi-views method for further exploration in semantic optimization.•This work validates the value of introducing multimodal approaches to visual emotion analysis. The validity of this method also provides the possibility to extend it to other visual understanding tasks.