Semi-Supervised Group Emotion Recognition Based on Contrastive Learning

Zhang, Jiayi; Wang, Xingzhi; Zhang, Dong; Lee, Dah-Jye

doi:10.3390/electronics11233990

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…In the future, to further improve its detection accuracy with the help of sufficiently large data samples, several advanced models such as contrastive learning approach [52], self-attention enhanced deep residual network [53], time-series sequencing method [54], multiscale superpixelwise prophet model [55], and multistage stepwise discrimination with compressed MobileNet [56] will be investigated into the assistance system. Furthermore, additional functions, such as emotion recognition and fatigue detection, will be designed to enhance the overall life quality of visually impaired people.…”

Section: Discussionmentioning

confidence: 99%

An AIoT-Based Assistance System for Visually Impaired People

Li,

Xie,

Chen

et al. 2023

Electronics

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In this work, an assistance system based on the Artificial Intelligence of Things (AIoT) framework was designed and implemented to provide convenience for visually impaired people. This system aims to be low-cost and multi-functional with object detection, obstacle distance measurement, and text recognition achieved by wearable smart glasses, heart rate detection, fall detection, body temperature measurement, and humidity-temperature monitoring offered by an intelligent walking stick. The total hardware cost is approximately $66.8, as diverse low-cost sensors and modules are embedded. Meanwhile, a voice assistant is adopted, which helps to convey detection results to users. As for the performance evaluation, the accuracies of object detection and text recognition in the wearable smart glasses experiments are 92.16% and 99.91%, respectively, and the maximum deviation rate compared to the mobile app on obstacle distance measurement is 6.32%. In addition, the intelligent walking stick experiments indicate that the maximum deviation rates compared to the commercial devices on heart rate detection, body temperature measurement, and humidity-temperature monitoring are 3.52%, 0.19%, and 3.13%, respectively, and the fall detection accuracy is 87.33%. Such results demonstrate that the proposed assistance system yields reliable performances similar to commercial devices and is impressive when considering the total cost as a primary concern. Consequently, it satisfies the fundamental requirements of daily life, benefiting the safety and well-being of visually impaired people.

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Section: Discussionmentioning

confidence: 99%

An AIoT-Based Assistance System for Visually Impaired People

Li,

Xie,

Chen

et al. 2023

Electronics

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“…Semi-supervised learning is an approach that combines a small set of labelled data with a larger set of unlabelled data during model training [9]. In the context of text classification and more particularly multi-label emotion classification, semi-supervised learning techniques have shown promise in addressing the challenge of limited annotated data [27], [28]. Existing works divide semi-supervised learning approaches into four main categories: graph-based, unsupervised preprocessing, intrinsically semi-supervised approaches, and wrapper methods.…”

Section: B Semi-supervised Learning For Text Classificationmentioning

confidence: 99%

Pseudo-Labeling With Large Language Models for Multi-Label Emotion Classification of French Tweets

Malik,

Bernard,

Pauchet

et al. 2024

IEEE Access

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This study proposes a novel semi-supervised multi-label emotion classification approach for French tweets based on pseudo-labeling. Human subjectivity in emotional expression makes it difficult for a machine to learn. Therefore, it necessitates training supervised learning models on large datasets annotated by multiple annotators. However, creating such datasets can be costly and time-consuming. Moreover, aggregating annotations from multiple annotators to capture their collective emotional state adds complexity to the task. Semi-supervised learning techniques have shown effectiveness with limited datasets. Furthermore, Large language Models (LLMs), particularly Chat-GPT, have demonstrated superior annotation accuracy compared to annotations obtained from crowdsourcing platforms, when both are evaluated against expertannotated data. This work introduces a novel approach for multi-label emotion classification of French tweets by leveraging pseudo-labels generated through Chat-GPT, a robust large language model. Using zero-shot, one-shot, and few-shot learning techniques, Chat-GPT annotates the unlabelled part of our dataset. These Chat-GPTannotated pseudo-labels are then merged with manual annotations, facilitating the training of a multilabel emotion classification model via semi-supervised learning. Furthermore, within the context of our research, we present a new French tweet dataset, containing testimonials from people affected by an urban industrial incident. This dataset features 2,350 tweets, each manually annotated by three human annotators based on 8 pre-identified emotions. Benchmark results are presented for multi-label emotion classification models employing both fully supervised and semi-supervised approaches with pseudo-labeling. Our findings demonstrate the superiority of our approach for multi-label emotion classification over standard pseudolabeling and an established baseline.

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“…On the other hand, the manual annotation of a large-scale dataset is highly labor-intensive and expensive. Therefore, semi-supervised learning [8][9][10][11][12][13][14][15][16], which involves training with a small amount of labeled data and a large amount of unlabeled data, is being investigated by more and more researchers. By incorporating both labeled and unlabeled data, semi-supervised learning can exploit the rich information in the unlabeled data and generate a more robust and accurate model, reducing its dependence on a large amount of labeled data.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Object Detection with Multi-Scale Regularization and Bounding Box Re-Prediction

Shao,

Lv,

Zhang

et al. 2024

Electronics

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Semi-supervised object detection has become a hot topic in recent years, but there are still some challenges regarding false detection, duplicate detection, and inaccurate localization. This paper presents a semi-supervised object detection method with multi-scale regularization and bounding box re-prediction. Specifically, to improve the generalization of the two-stage object detector and to make consistent predictions related to the image and its down-sampled counterpart, a novel multi-scale regularization loss is proposed for the region proposal network and the region-of-interest head. Then, in addition to using the classification probabilities of the pseudo-labels to exploit the unlabeled data, this paper proposes a novel bounding box re-prediction strategy to re-predict the bounding boxes of the pseudo-labels in the unlabeled images and select the pseudo-labels with reliable bounding boxes (location coordinates) to improve the model’s localization accuracy based on its unsupervised localization loss. Experiments on the public MS COCO and Pascal VOC show that our proposed method achieves a competitive detection performance compared to other state-of-the-art methods. Furthermore, our method offers a multi-scale regularization strategy and a reliably located pseudo-label screening strategy, both of which facilitate the development of semi-supervised object detection techniques and boost the object detection performance in autonomous driving, industrial inspection, and agriculture automation.

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Semi-Supervised Group Emotion Recognition Based on Contrastive Learning

Cited by 7 publications

References 36 publications

An AIoT-Based Assistance System for Visually Impaired People

An AIoT-Based Assistance System for Visually Impaired People

Pseudo-Labeling With Large Language Models for Multi-Label Emotion Classification of French Tweets

Semi-Supervised Object Detection with Multi-Scale Regularization and Bounding Box Re-Prediction

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