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Emotion Recognition

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하위 태스크: 12
평가표: 10
보존된 평가 기록: 18

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jarrycyx/unn

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CUTS+: High-dimensional Causal Discovery from Irregular Time-series

https://github.com/jasontslxd/pqwgan

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Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation

https://github.com/jayaneetha/Zeta-Policy

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A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition

https://github.com/jayaneetha/emoDARTS

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emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition

https://github.com/jbdel/OMG_UMONS_submission

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Transformer for Emotion Recognition

https://github.com/jbdel/modulated_fusion_transformer

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Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition

https://github.com/jcrichard/pyrb

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A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios

https://github.com/jech2/ym2413-mdb

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YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations

https://github.com/jedota/flickr-pad

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Flickr-PAD: New Face High-Resolution Presentation Attack Detection Database

https://github.com/jedyang97/MTAG

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MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences

https://github.com/jemmryx/EQG-RACE

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EQG-RACE: Examination-Type Question Generation

https://github.com/jet-net/jetnet

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Evaluating generative models in high energy physics

https://github.com/jiahongliu21/awesome-personalized-large-language-models

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A Survey of Personalized Large Language Models: Progress and Future Directions

https://github.com/jiaming-wang/HMB

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Pan-sharpening via High-pass Modification Convolutional Neural Network

https://github.com/jianboma/low_latency_attention_module

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A low latency attention module for streaming self-supervised speech representation learning

https://github.com/jiaozizhao/Two-in-One-ActionDetection

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Dance with Flow: Two-in-One Stream Action Detection

https://github.com/jiawangbai/HAT

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Improving Vision Transformers by Revisiting High-frequency Components

https://github.com/jiaxin-ye/emo-dna

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Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition

https://github.com/jiaxinxie97/hfgi3d

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High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization

https://github.com/jiaxiongq/neus-hsr

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Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections

https://github.com/jinyangjiangai/quantile-based-policy-optimization

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Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithms

https://github.com/jitao-xu/dual-decoding

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One Source, Two Targets: Challenges and Rewards of Dual Decoding

https://github.com/jjfeng/easier_net

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Ensembled sparse-input hierarchical networks for high-dimensional datasets

https://github.com/jjfeng/spinn

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Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classification

https://github.com/jmkuebler/auto-tst

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AutoML Two-Sample Test

https://github.com/jmkuebler/automl-tst-paper

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AutoML Two-Sample Test

https://github.com/jmkuebler/wits-test

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A Witness Two-Sample Test

https://github.com/joedaws/asgf

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An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization

https://github.com/johndmendonca/maia-dqe

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Dialogue Quality and Emotion Annotations for Customer Support Conversations

https://github.com/jongwon20000/TRACA

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Context-aware Deep Feature Compression for High-speed Visual Tracking

https://github.com/jsfunc/best-arm-identification

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Learning the distribution with largest mean: two bandit frameworks

https://github.com/juliamendelsohn/framing

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Modeling Framing in Immigration Discourse on Social Media

https://github.com/julian-urbano/sigir2019-statistical

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Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors

https://github.com/julianyulu/icassp2021-mscnn-spu

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Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention

https://github.com/julieeF/CS231N-Project

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Emotion Recognition in Audio and Video Using Deep Neural Networks

https://github.com/juliusberner/theory2practice

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Learning ReLU networks to high uniform accuracy is intractable

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