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Video Emotion Recognition
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논문
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Visual and textual prompts for enhancing emotion recognition in video
MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition
VEMOCLAP: A video emotion classification web application
FE-Adapter: Adapting Image-based Emotion Classifiers to Videos
MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition
Towards Emotion Analysis in Short-form Videos: A Large-Scale Dataset and Baseline
Affective Video Content Analysis: Decade Review and New Perspectives
Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children
Versatile audio-visual learning for emotion recognition
Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis
FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference
ICANet: A Method of Short Video Emotion Recognition Driven by Multimodal Data
Multi-modal Residual Perceptron Network for Audio-Video Emotion Recognition
Technical Report for Valence-Arousal Estimation on Affwild2 Dataset
Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition
Emotion Recognition in Audio and Video Using Deep Neural Networks
Audio-video Emotion Recognition in the Wild using Deep Hybrid Networks
An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos
MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition
Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition
Context-aware Cascade Attention-based RNN for Video Emotion Recognition
Automatic Recognition of Facial Displays of Unfelt Emotions
Spatiotemporal Networks for Video Emotion Recognition
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.