Titlebook: Computer Vision – ACCV 2022 Workshops; 16th Asian Conferenc Yinqiang Zheng,Hacer Yalim Kele?,Piotr Koniusz Conference proceedings 2023 The

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Vincent Grosso,Fran?ois-Xavier Standaertficiency of operations. However, this research area is still underrepresented compared to other automotive domains, especially regarding available image data, which is essential for training and benchmarking AI-based approaches. To mitigate this gap, we introduce a novel dataset specialized on stati

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Vincent Grosso,Fran?ois-Xavier Standaertpatial and/or temporal resolution issue. Most existing methods extensively exploit various hand-crafted priors to regularize the ill-posed hyperspectral reconstruction problem, and are incapable of handling wide spectral variety, often resulting in poor reconstruction quality. In recent year, deep c

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致敬

Jesper Buus Nielsen,Vincent Rijmen surpassed the human performance benchmarks on existing digit datasets, given that these datasets contain digits that have limited variability. In this paper, we introduce Caltech Football Numbers (CaltechFN), an image dataset of highly variable American football digits that aims to serve as a more

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234141.jpg

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Computer Vision – ACCV 2022 Workshops978-3-031-27066-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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https://doi.org/10.1007/978-3-031-27066-6computer networks; computer security; computer systems; computer vision; correlation analysis; data secur

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978-3-031-27065-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Miruna Rosca,Damien Stehlé,Alexandre Walleto-expression recognition across three (positive, negative, and surprise) and five (happiness, other, anger, contempt, and surprise) classes. When compared with the state-of-the-art, the results report a significant improvement in accuracy and the F1 score. The proposal is also robust against the unbalanced class sizes of the SAMM dataset.