no code implementations • 12 Apr 2024 • Yang Yang, Hongpeng Pan, Qing-Yuan Jiang, Yi Xu, Jinghui Tang
According to the findings, we further propose a novel importance sampling-based, element-wise joint optimization method, called Adaptively Mask Subnetworks Considering Modal Significance(AMSS).
4 code implementations • 28 Sep 2022 • Yang shen, Xuhao Sun, Xiu-Shen Wei, Qing-Yuan Jiang, Jian Yang
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks.
no code implementations • 4 Aug 2020 • Ming-Wei Li, Qing-Yuan Jiang, Wu-Jun Li
In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search.
no code implementations • ECCV 2020 • Quan Cui, Qing-Yuan Jiang, Xiu-Shen Wei, Wu-Jun Li, Osamu Yoshie
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects.
no code implementations • ICCV 2019 • Qing-Yuan Jiang, Yi He, Gen Li, Jian Lin, Lei Li, Wu-Jun Li
With the explosive growth of video data in real applications, near-duplicate video retrieval (NDVR) has become indispensable and challenging, especially for short videos.
no code implementations • 27 May 2019 • Ming-Wei Li, Qing-Yuan Jiang, Wu-Jun Li
In this paper, we propose a novel hashing method, called deep multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID.
no code implementations • 27 May 2019 • Qing-Yuan Jiang, Ming-Wei Li, Wu-Jun Li
Bucket search, also called hash lookup, can achieve fast query speed with a sub-linear time cost based on the inverted index table constructed from hash codes.
no code implementations • 26 Jul 2017 • Qing-Yuan Jiang, Wu-Jun Li
However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points.