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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/AmirSh15/SSL_graph_audio

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Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data

https://github.com/AmirSh15/graph_emotion_recognition

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Dynamic Emotion Modeling with Learnable Graphs and Graph Inception Network

https://github.com/ArnabKumarRoy02/ResEmoteNet

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ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition

https://github.com/Atlas200dk/sample-imageinpainting-HiFill

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Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting

https://github.com/BIDData/BIDMach

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SAME but Different: Fast and High-Quality Gibbs Parameter Estimation

https://github.com/BUGPLEASEOUT/LasHeR

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LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking

https://github.com/BUTSpeechFIT/BrnoLM

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Text Augmentation for Language Models in High Error Recognition Scenario

https://github.com/BaratiLab/Diffusion-based-Fluid-Super-resolution

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A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction

https://github.com/BeautyOfWeb/AffinityNet

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AffinityNet: semi-supervised few-shot learning for disease type prediction

https://github.com/BenoitWang/Speech_Emotion_Diarization

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Speech Emotion Diarization: Which Emotion Appears When?

https://github.com/BestActionNow/SemiSupBLI

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Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction

https://github.com/BorealisAI/mtmfrl

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Multi Type Mean Field Reinforcement Learning

https://github.com/BoyanJIANG/LoRD

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LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling

https://github.com/CEA-LIST/AD-RobustnessEval

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Evaluating Robustness over High Level Driving Instruction for Autonomous Driving

https://github.com/CHNPAT005/PCIJAPTG-A2XvsJSE

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Comparing the market microstructure between two South African exchanges

https://github.com/CIFASIS/exploiting-gan-internal-capacity

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Exploiting GAN Internal Capacity for High-Quality Reconstruction of Natural Images

https://github.com/CPJKU/audioLIME

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Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities

https://github.com/CVMI-Lab/UHDM

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Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing

https://github.com/CaiShilv/HiFi-VRIC

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High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation

https://github.com/CenekAlbl/TwoViewUnsynch

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On the Two-View Geometry of Unsynchronized Cameras

https://github.com/ChenhongyiYang/QueryDet-PyTorch

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QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

https://github.com/ChopinSharp/ref-nms

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Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding

https://github.com/Circuitscape/Circuitscape.jl

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Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions

https://github.com/ClaudiuCreanga/semeval-2024-task-8

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Transformer based neural networks for emotion recognition in conversations

https://github.com/CogComp/zoe

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Zero-Shot Open Entity Typing as Type-Compatible Grounding

https://github.com/Cold-Winter/BachGAN

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BachGAN: High-Resolution Image Synthesis from Salient Object Layout

https://github.com/CompVis/taming-transformers

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Taming Transformers for High-Resolution Image Synthesis

https://github.com/ConvLab/ConvLab-3/tree/emo_loop/convlab/dst/emodst/modeling

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From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue

https://github.com/CopurOnur/ED-MTT

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Engagement Detection with Multi-Task Training in E-Learning Environments

https://github.com/Cynetics/MSGNet

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Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects

https://github.com/DAI-Lab/SteganoGAN

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SteganoGAN: High Capacity Image Steganography with GANs

https://github.com/DPautoGAN/DPautoGAN

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Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning

https://github.com/Daikenan/LTMU

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High-Performance Long-Term Tracking with Meta-Updater

https://github.com/David3384/GI_2019

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Detecting and Reducing Bias in a High Stakes Domain

https://github.com/DavidHason/ser

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Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition

https://github.com/DeLightCMU/PSA

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Polarized Self-Attention: Towards High-quality Pixel-wise Regression

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.