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- W3097502401 abstract "Deep Learning models proved to be able to generate highly discriminative image descriptors, named deep features, suitable for similarity search tasks such as Person Re-Identification and Image Retrieval. Typically, these models are trained by employing high-resolution datasets, therefore reducing the reliability of the produced representations when low-resolution images are involved. The similarity search task becomes even more challenging in the cross-resolution scenarios, i.e., when a low-resolution query image has to be matched against a database containing descriptors generated from images at different, and usually high, resolutions. To solve this issue, we proposed a deep learning-based approach by which we empowered a ResNet-like architecture to generate resolution-robust deep features. Once trained, our models were able to generate image descriptors less brittle to resolution variations, thus being useful to fulfill a similarity search task in cross-resolution scenarios. To asses their performance, we used synthetic as well as natural low-resolution images. An immediate advantage of our approach is that there is no need for Super-Resolution techniques, thus avoiding the need to synthesize queries at higher resolutions." @default.
- W3097502401 created "2020-11-09" @default.
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- W3097502401 date "2020-01-01" @default.
- W3097502401 modified "2023-10-15" @default.
- W3097502401 title "Cross-Resolution Deep Features Based Image Search" @default.
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- W3097502401 doi "https://doi.org/10.1007/978-3-030-60936-8_27" @default.
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