# Learning Multi-level Deep Representations for Image Emotion Classification

arXiv: https://arxiv.org/abs/1611.07145 · HTML: https://mlsanity.com/paper/1611.07145/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2016.

## Built on by (most cited first, 30 of 31)

- [EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes](https://mlsanity.com/paper/2307.07961/) (2023)
- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2401.04608/) (2024)
- [EmoEdit: Evoking Emotions through Image Manipulation](https://mlsanity.com/paper/2405.12661/) (2024)
- [Stimuli-Aware Visual Emotion Analysis](https://mlsanity.com/paper/2109.01812/) (2021)
- [SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network](https://mlsanity.com/paper/2110.12334/) (2021)
- [UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries](https://mlsanity.com/paper/2507.23372/) (2025)
- [A Circular-Structured Representation for Visual Emotion Distribution Learning](https://mlsanity.com/paper/2106.12450/) (2021)
- [Dual-path Collaborative Generation Network for Emotional Video Captioning](https://mlsanity.com/paper/2408.03006/) (2024)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [Affective Image Editing: Shaping Emotional Factors via Text Descriptions](https://mlsanity.com/paper/2505.18699/) (2025)
- [Learning to Compose Diversified Prompts for Image Emotion Classification](https://mlsanity.com/paper/2201.10963/) (2022)
- [CoEmoGen: Towards Semantically-Coherent and Scalable Emotional Image Content Generation](https://mlsanity.com/paper/2508.03535/) (2025)
- [Computational Emotion Analysis From Images: Recent Advances and Future Directions](https://mlsanity.com/paper/2103.10798/) (2021)
- [Towards Deeper Emotional Reflection: Crafting Affective Image Filters with Generative Priors](https://mlsanity.com/paper/2512.17376/) (2025)
- [Seeking Subjectivity in Visual Emotion Distribution Learning](https://mlsanity.com/paper/2207.11875/) (2022)
- [EmoFeedback2: Reinforcement of Continuous Emotional Image Generation via LVLM-based Reward and Textual Feedback](https://mlsanity.com/paper/2511.19982/) (2025)
- [ViPE: Visualise Pretty-much Everything](https://mlsanity.com/paper/2310.10543/) (2023)
- [UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception](https://mlsanity.com/paper/2409.18877/) (2024)
- [Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data](https://mlsanity.com/paper/2208.11868/) (2022)
- [Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals](https://mlsanity.com/paper/2306.02845/) (2023)
- [SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition in Images](https://mlsanity.com/paper/2307.02773/) (2023)
- [StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning](https://mlsanity.com/paper/2308.03000/) (2023)
- [Bridging Visual Affective Gap: Borrowing Textual Knowledge by Learning from Noisy Image-Text Pairs](https://mlsanity.com/paper/2511.17103/) (2025)
- [What Can I Edit? Open-Ended Strategy Discovery and the Emotion Editability Landscape](https://mlsanity.com/paper/2607.23920/) (2026)
- [Binary Representation via Jointly Personalized Sparse Hashing](https://mlsanity.com/paper/2208.14883/) (2022)
- [Analysis of the use of color and its emotional relationship in visual creations based on experiences during the context of the COVID-19 pandemic](https://mlsanity.com/paper/2203.13770/) (2022)
- [A Deep Features-Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification](https://mlsanity.com/paper/2408.07922/) (2024)
- [SentiFormer: Metadata Enhanced Transformer for Image Sentiment Analysis](https://mlsanity.com/paper/2502.15322/) (2025)
- [Attributes-aware Visual Emotion Representation Learning](https://mlsanity.com/paper/2504.06578/) (2025)

## Abstract (arXiv)

In this paper, we propose a new deep network that learns multi-level deep representations for image emotion classification (MldrNet). Image emotion can be recognized through image semantics, image aesthetics and low-level visual features from both global and local views. Existing image emotion classification works using hand-crafted features or deep features mainly focus on either low-level visual features or semantic-level image representations without taking all factors into consideration. The proposed MldrNet combines deep representations of different levels, i.e. image semantics, image aesthetics, and low-level visual features to effectively classify the emotion types of different kinds of images, such as abstract paintings and web images. Extensive experiments on both Internet images and abstract paintings demonstrate the proposed method outperforms the state-of-the-art methods using deep features or hand-crafted features. The proposed approach also outperforms the state-of-the-art methods with at least 6% performance improvement in terms of overall classification accuracy.