# Affective Video Content Analysis: Decade Review and New Perspectives

arXiv: https://arxiv.org/abs/2310.17212 · HTML: https://mlsanity.com/paper/2310.17212/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 147 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis](https://mlsanity.com/paper/2002.05000/) (2020)
- [iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis](https://mlsanity.com/paper/2107.00285/) (2021)
- [SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild](https://mlsanity.com/paper/1901.02839/) (2019)
- [Transformer-based Multimodal Information Fusion for Facial Expression Analysis](https://mlsanity.com/paper/2203.12367/) (2022)
- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild](https://mlsanity.com/paper/1808.09568/) (2018)
- [A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition](https://mlsanity.com/paper/2203.14779/) (2022)
- [An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos](https://mlsanity.com/paper/2003.00832/) (2020)
- [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021)
- [Self-attention fusion for audiovisual emotion recognition with incomplete data](https://mlsanity.com/paper/2201.11095/) (2022)
- [EmoNets: Multimodal deep learning approaches for emotion recognition in video](https://mlsanity.com/paper/1503.01800/) (2015)
- [Global-Local Attention for Emotion Recognition](https://mlsanity.com/paper/2111.04129/) (2021)
- [Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition](https://mlsanity.com/paper/2203.13052/) (2022)
- [Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization](https://mlsanity.com/paper/1511.04798/) (2015)
- [Supervised Transformer Network for Efficient Face Detection](https://mlsanity.com/paper/1607.05477/) (2016)
- [Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss](https://mlsanity.com/paper/2207.11482/) (2022)
- [Facial Expression Recognition from World Wild Web](https://mlsanity.com/paper/1605.03639/) (2016)
- [Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition](https://mlsanity.com/paper/1901.04889/) (2019)
- [Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices](https://mlsanity.com/paper/1707.07180/) (2017)
- [Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild](https://mlsanity.com/paper/1906.02728/) (2019)

## Built on by (most cited first, 5 of 5)

- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [Visual and Textual Prompts in VLLMs for Enhancing Emotion Recognition](https://mlsanity.com/paper/2504.17224/) (2025)
- [AIM-Bench: Benchmarking and Improving Affective Image Manipulation via Fine-Grained Hierarchical Control](https://mlsanity.com/paper/2604.10454/) (2026)
- [Disentangling Hardness from Noise: An Uncertainty-Driven Model-Agnostic Framework for Long-Tailed Remote Sensing Classification](https://mlsanity.com/paper/2601.00278/) (2026)
- [Emotion Recognition in Sign Language Conversation](https://mlsanity.com/paper/2605.23328/) (2026)

## Abstract (arXiv)

Video content is rich in semantics and has the ability to evoke various emotions in viewers. In recent years, with the rapid development of affective computing and the explosive growth of visual data, affective video content analysis (AVCA) as an essential branch of affective computing has become a widely researched topic. In this study, we comprehensively review the development of AVCA over the past decade, particularly focusing on the most advanced methods adopted to address the three major challenges of video feature extraction, expression subjectivity, and multimodal feature fusion. We first introduce the widely used emotion representation models in AVCA and describe commonly used datasets. We summarize and compare representative methods in the following aspects: (1) unimodal AVCA models, including facial expression recognition and posture emotion recognition; (2) multimodal AVCA models, including feature fusion, decision fusion, and attention-based multimodal models; (3) model performance evaluation standards. Finally, we discuss future challenges and promising research directions, such as emotion recognition and public opinion analysis, human-computer interaction, and emotional intelligence.