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- W4287115541 abstract "The advent of social media platforms has been a catalyst for the development of digital photography that engendered a boom in vision applications. With this motivation, we introduce a large-scale dataset termed 'Photozilla', which includes over 990k images belonging to 10 different photographic styles. The dataset is then used to train 3 classification models to automatically classify the images into the relevant style which resulted in an accuracy of ~96%. With the rapid evolution of digital photography, we have seen new types of photography styles emerging at an exponential rate. On that account, we present a novel Siamese-based network that uses the trained classification models as the base architecture to adapt and classify unseen styles with only 25 training samples. We report an accuracy of over 68% for identifying 10 other distinct types of photography styles. This dataset can be found at https://trisha025.github.io/Photozilla/" @default.
- W4287115541 created "2022-07-25" @default.
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- W4287115541 date "2021-06-21" @default.
- W4287115541 modified "2023-09-30" @default.
- W4287115541 title "Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles" @default.
- W4287115541 doi "https://doi.org/10.48550/arxiv.2106.11359" @default.
- W4287115541 hasPublicationYear "2021" @default.
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