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Baohua Qiang

4 accepted papers

2023

Joint Robust Representation And Generalization Enhancement For Cross-Modality Person Re-Identification

ICASSP 2023accepted

Cross-modality person re-identification (cm-ReID) aims to match pedestrian images from visible and infrared cameras. Most existing methods ignore data bias due to different cameras and views and overlook the strong dependence between feature maps that hinders modal alignment. In this paper, we propo…

Cited by 0SourceScholar
2021

Deep Adversarial Quantization Network for Cross-Modal Retrieval

ICASSP 2021accepted

In this paper, we propose a seamless multimodal binary learning method for cross-modal retrieval. First, we utilize adversarial learning to learn modality-independent representations of different modalities. Second, we formulate loss function through the Bayesian approach, which aims to jointly maxi…

Cited by 0SourceScholar
2021

Distribution-Aware Hierarchical Weighting Method for Deep Metric Learning

ICASSP 2021accepted

In this paper, we propose distribution-aware hierarchical weighting (DHW) method for deep metric learning. First, we formulate the distributions of different classes according to the form of gaussian curves, and update distributions as the training process. Second, depending on the learnable distrib…

Cited by 0SourceScholar
2021

Drawing Order Recovery from Trajectory Components

ICASSP 2021accepted

In spite of widely discussed, drawing order recovery (DOR) from static images is still a great challenge task. Based on the idea that drawing trajectories are able to be recovered by connecting their trajectory components in correct orders, this work proposes a novel DOR method from static images. T…

Cited by 0SourceScholar