39 citations · 75 across the 5 of their papers we have counts for
5 papers
Orthonormal Product Quantization Network for Scalable Face Image Retrieval
Ming Zhang, Xuefei Zhe, Hong Yan
Existing deep quantization methods provided an efficient solution for large-scale image retrieval. However, the significant intra-class variations like pose, illumination, and expr…
Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval
Ming Zhang, Hong Yan
Deep supervised hashing for image retrieval has attracted researchers' attention due to its high efficiency and superior retrieval performance. Most existing deep supervised hashin…
Partial FC: Training 10 Million Identities on a Single Machine
Xiang An, Xuhan Zhu, Yang Xiao +7
Training face recognition models with millions of identities is challenging because classifier storage, logit memory, and computation grow linearly with the number of classes, even…
EasyQuant: Post-training Quantization via Scale Optimization
Di Wu, Qi Tang, Yongle Zhao +3
The 8 bits quantization has been widely applied to accelerate network inference in various deep learning applications. There are two kinds of quantization methods, training-based q…
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
Ming Zhang, Xuefei Zhe, Le Ou-Yang +2
Deep hashing models have been proposed as an efficient method for large-scale similarity search. However, most existing deep hashing methods only utilize fine-level labels for trai…