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awesome-dynamic-facial-expression-recognition's Introduction

Awesome Dynamic Facial Expression Recognition

Benchmarks

Comparison of State-of-the-Art

Methods Venues Years DFEW FERV39k MAFW
UAR WAR UAR WAR UAR WAR
C3D - - 42.74 53.54 22.68 31.69 31.17 42.25
P3D - - 43.97 54.47 23.20 33.39 - -
I3D-RGB - - 43.40 54.27 30.17 38.78 - -
3D-ResNet18 - - 46.52 58.27 26.67 37.57 - -
R(2+1)D - - 42.79 53.22 31.55 41.28 - -
ResNet18-LSTM - - 51.32 63.85 30.92 42.95 28.08 39.38
R18-ViT (w/ pre-train) - - 55.76 67.56 38.35 48.43 35.80 47.72
EC-STFL[1] ACM MM 2020 45.35 56.51 - - - -
Former-DFER[2] ACM MM 2021 53.69 65.70 37.20 46.85 31.16 43.27
NR-DFERNet[3] arXiv 2022 54.21 68.19 33.99 45.97 - -
DPCNet[4] ACM MM 2022 57.11 66.32 - - - -
T-ESFL[5] ACM MM 2022 - - - - 33.28 48.18
EST[6] PR 2023 53.94 65.85 - - - -
Logo-Form[7] ICASSP 2023 54.21 66.98 38.22 48.13 - -
GCA+IAL[8] AAAI 2023 55.71 69.24 35.82 48.54 - -
CLIPER[9] arXiv 2023 57.56 70.84 41.23 51.34 - -
MSCM[10] PR 2023 58.49 70.16 - - - -
AEN[11] CVPRW 2023 56.66 69.37 38.18 47.88 - -
M3DFEL[12] CVPR 2023 56.10 69.25 35.94 47.67 - -
MAE-DFER[13] ACM MM 2023 63.41 74.43 43.12 52.07 41.62 54.31
DFER-CLIP[14] BMVC 2023 59.61 71.25 41.27 51.65 39.89 52.55
S2D[15] Arxiv 2023 61.82 76.03 41.28 52.56 41.86 57.37
A$^3$lign-DFER[16] arXiv 2024 64.09 74.02 41.87 51.79 42.07 53.24

References

2020

  1. Jiang X, Zong Y, Zheng W, Tang C, Xia W, Lu C, Liu J. DFEW: A Large-scale Database for Recognizing Dynamic Facial Expressions in the Wild. ACM MM, 2020. [Paper]

2021

  1. Zhao Z, Liu Q. Former-DFER: Dynamic Facial Expression Recognition Transformer. ACM MM, 2021. [Paper] [Code]

2022

  1. Li H, Sui M, Zhu Z. NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition. arXiv, 2022.[Paper]
  2. Wang Y, Sun Y, Song W, Gao S, Huang Y, Chen Z, Ge W, Zhang W. DPCNet: Dual Path Multi-Excitation Collaborative Network for Facial Expression Representation Learning in Videos. ACM MM, 2022. [Paper]
  3. Liu Y, Dai W, Feng C, Wang W, Yin G, Zeng J, Shan S. MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild. ACM MM, 2022. [Paper]

2023

  1. Liu Y, Wang W, Feng C, Zhang H, Chen Z, Zhan Y. Expression snippet transformer for robust video-based facial expression recognition. Pattern Recognition. Pattern Recognition, 2023. [Paper]
  2. Ma F, Sun B, Li S. Logo-Former: Local-Global Spatio-Temporal Transformer for Dynamic Facial Expression Recognition. ICASSP, 2023. [Paper]
  3. Li H, Niu H, Zhu Z, Zhao F. Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild. AAAI, 2023. [Paper] [Code]
  4. Li H, Niu H, Zhu Z, Zhao F. CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition. arXiv, 2023. [Paper]
  5. Li T, Chan KL, Tjahjadi T. Multi-Scale Correlation Module for Video-based Facial Expression Recognition in the Wild. Pattern Recognition, 2023. [Paper]
  6. Lee B, Shin H, Ku B, Ko H. Frame Level Emotion Guided Dynamic Facial Expression Recognition With Emotion Grouping. CVPRW, 2023. [Paper]
  7. Wang H, Li B, Wu S, Shen S, Liu F, Ding S, Zhou A. Rethinking the Learning Paradigm for Dynamic Facial Expression Recognition. CVPR, 2023. [Paper] [Code]
  8. Sun L, Lian Z, Liu B, Tao J. MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition. ACM MM, 2023. [Paper] [Code]
  9. Zhao Z, Patras I. Prompting Visual-Language Models for Dynamic Facial Expression Recognition. BMVC, 2023. [Paper] [Code]
  10. Chen, Yin, et al. "From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos." arXiv preprint arXiv:2312.05447 (2023). [Paper] [Code]
  11. Tao, Zeng, et al. "A $^{3} $ lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP." arXiv preprint arXiv:2403.04294 (2024) [Paper]

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