有偏见
发表于 2025-3-25 04:19:11
Probability Ordinal-Preserving Semantic Hashing,ten neglecting the mutual triplet-level ordinal structure crucial for similarity preservation. This chapter introduces a groundbreaking approach–the Probability Ordinal-preserving Semantic Hashing (POSH) framework–pioneering ordinal-preserving hashing under a non-parametric Bayesian theory. The fram
在驾驶
发表于 2025-3-25 10:09:31
Ordinal-Preserving Latent Graph Hashing, samples in the visual space to generate discriminative hash codes. However, these approaches neglect the intrinsic latent features within the high-dimensional feature space, making it challenging to capture the underlying topological structure of data and resulting in suboptimal hash codes for imag
Host142
发表于 2025-3-25 15:37:30
Deep Collaborative Graph Hashing,and the computational efficiency of compact hash code learning. However, existing deep semantic-preserving hashing approaches often treat semantic labels as ground truth for classification or transform them into prevalent pairwise similarities, overlooking interactive correlations between visual sem
免除责任
发表于 2025-3-25 17:55:39
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Isolate
发表于 2025-3-25 20:14:11
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Catheter
发表于 2025-3-26 01:40:41
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梯田
发表于 2025-3-26 05:52:14
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椭圆
发表于 2025-3-26 10:11:59
978-981-97-2114-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
Insubordinate
发表于 2025-3-26 14:08:40
Zheng ZhangBroadens the understanding of binary representation learning in the context of visual data.Offers the latest research trends in binary representation, modeling and learning.Expounds the potential, int
molest
发表于 2025-3-26 20:25:07
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