Visual emotion analysis(VEA) aiming to detect the emotions behind images, has gained increasing attention with the development of online social media. Recent studies in prompt learning have significantly advanced visual emotion classification. However, these methods usually utilize random vectors or non-emotional texts as the initialization for prompt optimization. This restricts the emotional semantic representation of prompts and hinders the performance of the model. To tackle this problem, we leverage emotional prompts with multiple views to enhance the semantic emotional information. We first translate the image to caption as context prompt(COP) from the view of background information for the image. Additionally, we introduce hybrid emotion prompt(HEP) from the view of the interaction between the emotional visual and textual information, where different modalities are integrated with a novel Emotion Joint Congruity Learning module. Furthermore, we also provide label prompt(LP) to enhance the emotional association with labels, enabling better emotional information fusion. Extensive experiments conducted on five publicly visual emotion classification datasets, i.e. EmoSet, FI, have demonstrated the superiority of our MVP model over cutting-edge methods.

中文翻译：

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学习多视角情感提示特征，进行视觉情感分析

视觉情感分析（VEA）旨在检测图像背后的情感，随着在线社交媒体的发展受到越来越多的关注。最近关于即时学习的研究显着推进了视觉情感分类。然而，这些方法通常利用随机向量或非情感文本作为提示优化的初始化。这限制了提示的情感语义表示并阻碍了模型的性能。为了解决这个问题，我们利用多视图的情感提示来增强语义情感信息。我们首先从图像的背景信息的角度将图像翻译为标题作为上下文提示（COP）。此外，我们从情感视觉和文本信息之间的交互角度引入了混合情感提示（HEP），其中不同的模式与新颖的情感联合一致性学习模块相集成。此外，我们还提供标签提示（LP）来增强与标签的情感关联，从而实现更好的情感信息融合。在五个公开的视觉情感分类数据集（即 EmoSet、FI）上进行的大量实验证明了我们的 MVP 模型相对于前沿方法的优越性。