Web18 de jul. de 2024 · Go Irie, Hiroyuki Arai, and Yukinobu Taniguchi. 2015. Alternating Co-Quantization for Cross-Modal Hashing. In IEEE International Conference on Computer Vision. 1886--1894. Google Scholar Digital Library; Qing-Yuan Jiang and Wu-Jun Li. 2016. Deep Cross-Modal Hashing. Computing Research Repository, Vol. abs/1602.02255 … WebMeta Cross-Modal Hashing on Long-Tailed Data ... (MetaCMH) based on meta learning for long-tail datasets. 2.2 Meta-learning To date, no single or general def-inition of meta …
Yongxin Wang (王永欣)
WebDue to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor search of multi-modal data. Most hashing methods assume that training data is class-balanced. However, in practice, real world data often have a long-tailed … Weblong-tail hashing cannot be solved simply by reweighting different classes in the loss function, but the combination of extended dy-namic meta-embedding, cross-entropy … dehydrated peas
Semantic-rebased cross-modal hashing for scalable unsupervised …
Web5 de jun. de 2024 · Jian Zhang, Yuxin Peng, and Mingkuan Yuan. 2024b. SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network. CoRR, Vol. abs/1802.02488 (2024). arxiv: 1802.02488 Google Scholar; Jian Zhang, Yuxin Peng, and Mingkuan Yuan. 2024c. Unsupervised Generative Adversarial Cross-Modal Hashing. In … WebExisting Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several … Web26 de mai. de 2024 · In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the performance degradation issue caused by binary optimization for hashing, we propose a novel momentum optimizer that performs hashing operation … dehydrated peas and corn