Vision Transformer With Attentive Pooling for Robust Facial Expression Recognition Article Swipe
Fanglei Xue
,
Qiangchang Wang
,
Zichang Tan
,
Zhongsong Ma
,
Guodong Guo
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/taffc.2022.3226473
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/taffc.2022.3226473
Facial Expression Recognition (FER) in the wild is an extremely challenging\ntask. Recently, some Vision Transformers (ViT) have been explored for FER, but\nmost of them perform inferiorly compared to Convolutional Neural Networks\n(CNN). This is mainly because the new proposed modules are difficult to\nconverge well from scratch due to lacking inductive bias and easy to focus on\nthe occlusion and noisy areas. TransFER, a representative transformer-based\nmethod for FER, alleviates this with multi-branch attention dropping but brings\nexcessive computations. On the contrary, we present two attentive pooling (AP)\nmodules to pool noisy features directly. The AP modules include Attentive Patch\nPooling (APP) and Attentive Token Pooling (ATP). They aim to guide the model to\nemphasize the most discriminative features while reducing the impacts of less\nrelevant features. The proposed APP is employed to select the most informative\npatches on CNN features, and ATP discards unimportant tokens in ViT. Being\nsimple to implement and without learnable parameters, the APP and ATP\nintuitively reduce the computational cost while boosting the performance by\nONLY pursuing the most discriminative features. Qualitative results demonstrate\nthe motivations and effectiveness of our attentive poolings. Besides,\nquantitative results on six in-the-wild datasets outperform other\nstate-of-the-art methods.\n
Related Topics
Concepts
Pooling
Artificial intelligence
Discriminative model
Computer science
Convolutional neural network
Transformer
Boosting (machine learning)
Pattern recognition (psychology)
Security token
Machine learning
Computation
Facial expression
Speech recognition
Engineering
Voltage
Algorithm
Computer security
Electrical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/taffc.2022.3226473
- OA Status
- green
- Cited By
- 108
- References
- 123
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311414773
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311414773Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/taffc.2022.3226473Digital Object Identifier
- Title
-
Vision Transformer With Attentive Pooling for Robust Facial Expression RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-05Full publication date if available
- Authors
-
Fanglei Xue, Qiangchang Wang, Zichang Tan, Zhongsong Ma, Guodong GuoList of authors in order
- Landing page
-
https://doi.org/10.1109/taffc.2022.3226473Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2212.05463Direct OA link when available
- Concepts
-
Pooling, Artificial intelligence, Discriminative model, Computer science, Convolutional neural network, Transformer, Boosting (machine learning), Pattern recognition (psychology), Security token, Machine learning, Computation, Facial expression, Speech recognition, Engineering, Voltage, Algorithm, Computer security, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
108Total citation count in OpenAlex
- Citations by year (recent)
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2025: 31, 2024: 57, 2023: 20Per-year citation counts (last 5 years)
- References (count)
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123Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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