https://github.com/shuvenduroy/ssl_fer
공식 코드Analysis of Semi-Supervised Methods for Facial Expression Recognition
Papers with Code 아카이브
Facial Expression Recognition (FER)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 5
- 평가표
- 25
- 보존된 평가 기록
- 167
Facial Expression Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.
<span style="color:grey; opacity: 0.6">( Image credit: DeXpression )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shuvenduroy/ssl_fer_ood
공식 코드https://github.com/siddiquelatif/URDU-Dataset
공식 코드Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
https://github.com/spaghettisystems/emotion_whisper
공식 코드https://github.com/sridharsola/msd-e
공식 코드https://github.com/starhiking/ExpLLM-TMM
공식 코드ExpLLM: Towards Chain of Thought for Facial Expression Recognition
https://github.com/swjtuma/fer-yolo-mamba
공식 코드FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space
https://github.com/takihasan/arbex
공식 코드ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
https://github.com/talented-q/ifer-master
공식 코드More comprehensive facial inversion for more effective expression recognition
https://github.com/talented-q/poster_v2
공식 코드POSTER++: A simpler and stronger facial expression recognition network
https://github.com/taylor-xy0827/Ada-DF
공식 코드Ada-DF: An Adaptive Label Distribution Fusion Network For Facial Expression Recognition
https://github.com/thorbenortmann/emohevrdb-sfer
공식 코드https://github.com/thorbenortmann/emoji-hero-vr-database
공식 코드https://github.com/tvaranka/meb
공식 코드Data Leakage and Evaluation Issues in Micro-Expression Analysis
https://github.com/uark-cviu/micron-bert
공식 코드https://github.com/usef-kh/fer
공식 코드Facial Emotion Recognition: State of the Art Performance on FER2013
https://github.com/vigor-jzhang/dysphonic-emb
공식 코드Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection
https://github.com/wagner-niklas/cage_expression_inference
공식 코드https://github.com/wangyanckxx/surveyfer
공식 코드A Survey on Facial Expression Recognition of Static and Dynamic Emotions
https://github.com/wangzhifengharrison/htnet
공식 코드https://github.com/xinqi-fan/ABAW2021
공식 코드Spatial and Temporal Networks for Facial Expression Recognition in the Wild Videos
https://github.com/xjtupanda/auw-gcn
공식 코드AU-aware graph convolutional network for Macro- and Micro-expression spotting
https://github.com/xxtars/emo-llama
공식 코드EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
https://github.com/yaoing/dan
공식 코드Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition
https://github.com/yelboudouri/EmoNeXt
공식 코드https://github.com/youqingxiaozhua/apvit
공식 코드Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition
https://github.com/yyx-future/la-cmfer
공식 코드https://github.com/zczcwh/poster
공식 코드POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
https://github.com/zengqunzhao/exp-clip
공식 코드Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer
https://github.com/zeroqiaoba/gpt4v-emotion
공식 코드GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
https://github.com/zerosansan/fg2020_abaw
공식 코드Deep Convolutional Neural Network Based Facial Expression Recognition in the Wild
https://github.com/zfkarl/leaf
공식 코드LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression Recognition
https://github.com/zhelishisongjie/ssfer
공식 코드https://github.com/zhengqinlai/imer-benchmark
공식 코드https://github.com/zimzsh/cross-corpus-readability-compatibility-assessment-for-english-texts
공식 코드Cross-corpus Readability Compatibility Assessment for English Texts
https://github.com/zouxinyi0625/cdnet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.