Search Results for author: Xiaobin Liu

Found 6 papers, 2 papers with code

Factor Decomposed Generative Adversarial Networks for Text-to-Image Synthesis

no code implementations24 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.

Image Generation Sentence +2

Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification

1 code implementation11 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

Domain Adaptive Person Re-Identification via Coupling Optimization

1 code implementation6 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

Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs

no code implementations25 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).

RAM: A Region-Aware Deep Model for Vehicle Re-Identification

no code implementations25 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.

Vehicle Re-Identification

E$^2$BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network

no code implementations18 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).

Image Retrieval Quantization +1

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