zero-shot hashing (ZSH)zero-shot learning (ZSL)Zero-shot hashing (ZSH) aims at learning a hashing model that is trained only by instances from seen categories but can generate well to those of unseen categories. Typically, it is achieved by utilizing a semantic embedding ...
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and testing set are identical. However, the assumption is often violated, causing a zero-shot CMH problem. Recent efforts to...
His research interests include multimodal learning, cross-modal retrieval, cross-modal hashing, and zero-shot learning. Zhikui Chen received his Ph.D. degree in Digital Signal Processing and M.S. degree in Mechanics from Chongqing University, China, in 1998 and 1993, respectively. He obtained ...
& Gong, S. Semantic autoencoder for zero-shot learning. IEEE. 1–10 (2017). Zhou, J., Ding, G. & Guo, Y. Latent semantic sparse hashing for cross-modal similarity search. ACM. 1–5 (2014). Wu, Y., Wang, S. & Huang, Q. Multi-modal semantic autoencoder for cross-modal ...
Unpaired robust hashing with noisy labels for zero-shot cross-modal retrieval With new social media concepts emerging, zero-shot cross-modal retrieval methods have gained significant attention. Most of the existing methods assume tha... K Yong,Z Shu,Z Yu - 《Engineering Applications of Artificial...
To address the challenge of zero-shot cross-modal retrieval, we propose an orthogonal method in this paper, i.e., Cross-modal Hashing with Orthogonal Projection (CHOP). It projects cross-modal features and class attributes onto a Hamming space, where each projection of cross-modal features is...
Zero-shot learningCross-modal hashing has drawn increasing research interests in cross-modal retrieval due to the explosive growth of multimedia big data. However, most of the existing models are trained and tested in a close-set circumstance, which may easily fail on the newly emerged con-cep...