Emotion analysis, a fundamental component of human-computer interaction, influences various domains, including content recommendation, image generation, and psychological research. Images and music, as crystallizations of human culture, inherently carry the emotions embedded by their creators. Analyzing the emotions conveyed in these works has long been a prominent direction of exploration in the field. Recent research in emotion analysis can be broadly categorized into two main streams: emotion label classification and valence-arousal prediction. My work primarily focuses on valence-arousal prediction. Valence represents the pleasure or displeasure elicited by a stimulus, while arousal indicates the degree of excitement or calmness. Both these metrics are crucial for the expression of human emotions.