Classification of Facial Expression In-the-Wild based on Ensemble of Multi-head Cross Attention Networks

Jeong, Jae -Yeop; Hong, Yeong-Gi; Kim, Daun; Jeong, Jinwoo; Jung, Yuchul; Kim, Sang-Ho

doi:10.1109/cvprw56347.2022.00262

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…Second, time-shifting and time-scaling methods further enrich the diversity of the dataset by performing shifting and stretching operations on the time-series data to simulate fault scenarios at different time scales. In addition, noise injection techniques [38] simulate real-world environmental disturbances and noises, which helps to improve the noise immunity of the model. However inappropriate noise injection strategies may instead lead to disturbed model performance.…”

Section: Fault Recording Data Augmentationmentioning

confidence: 99%

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

Jia,

Huang,

Han

et al. 2024

IET Communications

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Fault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.

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Section: Fault Recording Data Augmentationmentioning

confidence: 99%

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

Jia,

Huang,

Han

et al. 2024

IET Communications

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show abstract

“…Zhang et al [53]utilized the multimodal information from the images, audio and text and proposed a unified multimodal framework to fully use the emotion information, which achieved the best performance in ABAW3 competition. Jeong et al [15]proposed a multi-head cross attention networks and pretrained on Glint360K [1] and some private commercial datasets. Xue et al [46] proposed the Coarse-to-Fine Cascaded networks (CFC) to address the label ambiguity problem and used smooth predicting method to post-process the extracted features.…”

Section: Affective Behavior Analysis In-the-wildmentioning

confidence: 99%

Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition

Wang¹,

Cai²,

Li³

et al. 2023

Preprint

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Facial Expression Recognition (FER) is an important task in computer vision and has wide applications in human-computer interaction, intelligent security, emotion analysis, and other fields. However, the limited size of FER datasets limits the generalization ability of expression recognition models, resulting in ineffective model performance. To address this problem, we propose a semisupervised learning framework that utilizes unlabeled face data to train expression recognition models effectively. Our method uses a dynamic threshold module (DTM) that can adaptively adjust the confidence threshold to fully utilize the face recognition (FR) data to generate pseudo-labels, thus improving the model's ability to model facial expressions. In the ABAW5 EXPR task, our method achieved excellent results on the official validation set.

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ABAW: Learning from Synthetic Data & Multi-task Learning Challenges

Kollias

2023

Lecture Notes in Computer Science

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Classification of Facial Expression In-the-Wild based on Ensemble of Multi-head Cross Attention Networks

Cited by 10 publications

References 15 publications

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition

ABAW: Learning from Synthetic Data & Multi-task Learning Challenges

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