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Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez |
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Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition |
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Journal Article |
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2023 |
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ACM Transactions on Multimedia Computing, Communications, and Applications |
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TMCCA |
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20 |
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2 |
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1–20 |
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Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
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Admin @ si @ GZW2023 |
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3862 |
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Author |
Wenwen Fu; Zhihong An; Wendong Huang; Haoran Sun; Wenjuan Gong; Jordi Gonzalez |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection |
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Journal Article |
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2023 |
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Electronics |
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ELEC |
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12 |
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18 |
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3947 |
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micro-expression spotting; sliding window; key frame extraction |
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Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores. |
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Admin @ si @ FAH2023 |
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3864 |
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Diego Velazquez; Pau Rodriguez; Alexandre Lacoste; Issam H. Laradji; Xavier Roca; Jordi Gonzalez |
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Title |
Evaluating Counterfactual Explainers |
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2023 |
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Transactions on Machine Learning Research |
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TMLR |
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Explainability; Counterfactuals; XAI |
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Explainability methods have been widely used to provide insight into the decisions made by statistical models, thus facilitating their adoption in various domains within the industry. Counterfactual explanation methods aim to improve our understanding of a model by perturbing samples in a way that would alter its response in an unexpected manner. This information is helpful for users and for machine learning practitioners to understand and improve their models. Given the value provided by counterfactual explanations, there is a growing interest in the research community to investigate and propose new methods. However, we identify two issues that could hinder the progress in this field. (1) Existing metrics do not accurately reflect the value of an explainability method for the users. (2) Comparisons between methods are usually performed with datasets like CelebA, where images are annotated with attributes that do not fully describe them and with subjective attributes such as ``Attractive''. In this work, we address these problems by proposing an evaluation method with a principled metric to evaluate and compare different counterfactual explanation methods. The evaluation method is based on a synthetic dataset where images are fully described by their annotated attributes. As a result, we are able to perform a fair comparison of multiple explainability methods in the recent literature, obtaining insights about their performance. We make the code public for the benefit of the research community. |
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Admin @ si @ VRL2023 |
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3891 |
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