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- W4386159021 abstract "Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utilize weighted sum or concatenation to fuse the multi-view features. We argue that these fusion methods cannot capture the interaction among different views. Furthermore, these methods ignored the information provided by the dissimilar samples. We propose a novel deep metric multi-view hashing (DMMVH) method to address the mentioned problems. Extensive empirical evidence is presented to show that gate-based fusion is better than typical methods. We introduce deep metric learning to the multi-view hashing problems, which can utilize metric information of dissimilar samples. On the MIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the current state-of-the-art methods by a large margin (up to 15.28 mean Average Precision (mAP) improvement)." @default.
- W4386159021 created "2023-08-26" @default.
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- W4386159021 date "2023-07-01" @default.
- W4386159021 modified "2023-09-26" @default.
- W4386159021 title "Deep Metric Multi-View Hashing for Multimedia Retrieval" @default.
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- W4386159021 doi "https://doi.org/10.1109/icme55011.2023.00335" @default.
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