no code implementations • 24 Mar 2023 • Jiguo Li, Xiaobin Liu, Lirong Zheng
Prior works about text-to-image synthesis typically concatenated the sentence embedding with the noise vector, while the sentence embedding and the noise vector are two different factors, which control the different aspects of the generation.
1 code implementation • 11 May 2021 • Xiaobin Liu, Shiliang Zhang
Specifically, given unlabeled training images, we apply teacher networks to extract corresponding features and further construct a teacher graph for each teacher network to describe the similarity relationships among training images.
Contrastive Learning Domain Adaptive Person Re-Identification +2
1 code implementation • 6 Nov 2020 • Xiaobin Liu, Shiliang Zhang
Extensive experiments on three large-scale datasets, i. e., Market-1501, DukeMTMC-reID, and MSMT17, show that our coupling optimization outperforms state-of-the-art methods by a large margin.
Domain Adaptive Person Re-Identification Transfer Learning +1
no code implementations • 25 May 2020 • Liang Jiang, Xiaobin Liu, Peter C. B. Phillips, Yichong Zhang
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs).
no code implementations • 25 Jun 2018 • Xiaobin Liu, Shiliang Zhang, Qingming Huang, Wen Gao
Specifically, in addition to extracting global features, RAM also extracts features from a series of local regions.
no code implementations • 18 Sep 2017 • Xiaobin Liu, Shiliang Zhang, Tiejun Huang, Qi Tian
To conquer these issues, we propose an End-to-End BoWs (E$^2$BoWs) model based on Deep Convolutional Neural Network (DCNN).