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- W4386065601 abstract "For learning-based binary descriptors, the binarization process has not been well addressed. The reason is that the binarization blocks gradient back-propagation. Existing learning-based binary descriptors learn real-valued output, and then it is converted to binary descriptors by their proposed binarization processes. Since their binarizaiion processes are not a component of the network, the learning-based binary descriptor cannot fully utilize the advances of deep learning. To solve this issue, we propose a model-agnostic plugin binary transformation layer (BTL), making the network directly generate binary descriptors. Then, we present the first self-supervised, direct-learned binary descriptor, dubbed DLBD. Furthermore, we propose ultra-wide temperature-scaled crossentropy loss to adjust the distribution of learned descriptors in a larger range. Experiments demonstrate that the proposed BTL can substitute the previous binarization process. Our proposed DLBD outperforms SOTA on different tasks such as image retrieval and classification <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> Our code is available at: https://github.com/CQUPT-CV/DLBD." @default.
- W4386065601 created "2023-08-23" @default.
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- W4386065601 date "2023-06-01" @default.
- W4386065601 modified "2023-09-27" @default.
- W4386065601 title "DLBD: A Self-Supervised Direct-Learned Binary Descriptor" @default.
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- W4386065601 doi "https://doi.org/10.1109/cvpr52729.2023.01521" @default.
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