# Global Image Sentiment Transfer

arXiv: https://arxiv.org/abs/2006.11989 · HTML: https://mlsanity.com/paper/2006.11989/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Regressor-Guided Image Editing Shifts Emotion and Disengagement Timing in Social Media](https://mlsanity.com/paper/2501.12289/) (2025)
- [Music Sentiment Transfer](https://mlsanity.com/paper/2110.05765/) (2021)
- [EmoLat: Text-driven Image Sentiment Transfer via Emotion Latent Space](https://mlsanity.com/paper/2601.12079/) (2026)
- [Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video](https://mlsanity.com/paper/2609.21624/) (2026)

## Abstract (arXiv)

Transferring the sentiment of an image is an unexplored research topic in the area of computer vision. This work proposes a novel framework consisting of a reference image retrieval step and a global sentiment transfer step to transfer sentiments of images according to a given sentiment tag. The proposed image retrieval algorithm is based on the SSIM index. The retrieved reference images by the proposed algorithm are more content-related against the algorithm based on the perceptual loss. Therefore can lead to a better image sentiment transfer result. In addition, we propose a global sentiment transfer step, which employs an optimization algorithm to iteratively transfer sentiment of images based on feature maps produced by the Densenet121 architecture. The proposed sentiment transfer algorithm can transfer the sentiment of images while ensuring the content structure of the input image intact. The qualitative and quantitative experiments demonstrate that the proposed sentiment transfer framework outperforms existing artistic and photorealistic style transfer algorithms in making reliable sentiment transfer results with rich and exact details.