Hetero-Center loss for cross-modality person Re-identification

Zhu, Yuanxin; Zhao, Yang; Wang, Li; Zhao, Sai; Hu, Xiao; Tao, Dapeng

doi:10.1016/j.neucom.2019.12.100

Cited by 167 publications

(109 citation statements)

References 37 publications

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“…As a result, a cross-modality model can learn to capture information shared by different modalities during the training process. More details of the hetero-centre loss can be refferred to [48]. Dispite using the IMSLoss for refined multimodality feature learning, the cross-entropy loss L ce (Eq.…”

Section: Inter-modality Similarity Lossmentioning

confidence: 99%

Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction

Zhou

Zhang

Zhu

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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The outbreak of coronavirus disease has been a nightmare to citizens, hospitals, healthcare practitioners, and the economy in 2020. The overwhelming number of confirmed cases and suspected cases put forward an unprecedented challenge to the hospital's capacity of management and medical resource distribution. To reduce the possibility of crossinfection and attend a patient according to his severity level, expertly diagnosis and sophisticated medical examinations are often required but hard to fulfil during a pandemic. To facilitate the assessment of a patient's severity, this paper proposes a multi-modality feature learning and fusion model for end-toend covid patient severity prediction using the blood test supported electronic medical record (EMR) and chest computerized tomography (CT) scan images. To evaluate a patient's severity by the co-occurrence of salient clinical features, the High-order Factorization Network (HoFN) is proposed to learn the impact of a set of clinical features without tedious feature engineering. On the other hand, an attention-based deep convolutional neural network (CNN) using pre-trained parameters are used to process the lung CT images. Finally, to achieve cohesion of cross-modality representation, we design a loss function to shift deep features of both-modality into the same feature space which improves the model's performance and robustness when one modality is absent. Experimental results demonstrate that the proposed multi-modality feature learning and fusion model achieves high performance in an authentic scenario.

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Section: Inter-modality Similarity Lossmentioning

confidence: 99%

Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction

Zhou

Zhang

Zhu

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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“…By considering that our proposed method in this paper is closely related to loss function, we only give a rough introduction of person reidentification methods based on loss function. It is worth noting that many other inspiring methods have been proposed to address the person reidentification tasks, e.g., pose-guided methods [26], crossmodality based methods [27], and unsupervised learning based methods [28]. One can learn about more detailed information in [29].…”

Section: Related Workmentioning

confidence: 99%

Circle-Based Ratio Loss for Person Reidentification

Zhao

Liu

et al. 2020

Complexity

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Person reidentification (re-id) aims to recognize a specific pedestrian from uncrossed surveillance camera views. Most re-id methods perform the retrieval task by comparing the similarity of pedestrian features extracted from deep learning models. Therefore, learning a discriminative feature is critical for person reidentification. Many works supervise the model learning with one or more loss functions to obtain the discriminability of features. Softmax loss is one of the widely used loss functions in re-id. However, traditional softmax loss inherently focuses on the feature separability and fails to consider the compactness of within-class features. To further improve the accuracy of re-id, many efforts are conducted to shrink within-class discrepancy as well as between-class similarity. In this paper, we propose a circle-based ratio loss for person re-identification. Concretely, we normalize the learned features and classification weights to map these vectors in the hypersphere. Then we take the ratio of the maximal intraclass distance and the minimal interclass distance as an objective loss, so the between-class separability and within-class compactness can be optimized simultaneously during the training stage. Finally, with the joint training of an improved softmax loss and the ratio loss, the deep model could mine discriminative pedestrian information and learn robust features for the re-id task. Comprehensive experiments on three re-id benchmark datasets are carried out to illustrate the effectiveness of the proposed method. Specially, 83.12% mAP on Market-1501, 71.70% mAP on DukeMTMC-reID, and 66.26%/63.24% mAP on CUHK03 labeled/detected are achieved, respectively.

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“…Recently, some scholars have investigated a better loss function. For example, Zhu et al [8] created a heterogenous Hetero-Center (HC) loss to narrow the gaps between images of the two modalities.…”

Section: Introductionmentioning

confidence: 99%

Dual-Path Deep Supervision Network with Self-Attention for Visible-Infrared Person Re-Identification

Cheng

Xiao

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Visible-infrared person re-identification(VI-ReID) is an emerging but challenging problem that aims to match pedestrians captured by visible and infrared cameras. Existing studies in this field mainly focus on learning sharable feature representations from the last layer of deep convolution neural networks (CNNs) to handle the cross-modality discrepancies. However, due to the huge differences between visible and infrared images, the last layer's feature representations are less discriminative for VI-ReID. To remedy this, we propose a novel deep supervision learning network, namely Dual-path Deep Supervision Network (DDSN), for VI-ReID. Based on the backbone network, DDSN consists of two key modules, (1) a dual-path deep supervision learning (DDSL) module that is plugged into multiple network layers, and (2) a self-attention module that is developed on top of the backbone network. The backbone network extracts multilevel features at lower middle layers, and several DDSL modules utilize these features to generate more discriminative descriptors. Furthermore, we apply the self-attention module for context modeling to capture useful contextual cues as a supplement. By fusing these descriptors, DDSN can utilize both multi-level information and potential contextual information. Despite the apparent simplification, our method outperforms several state-ofthe-art methods on two large-scale datasets: RegDB and SYSU-MM01.

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Hetero-Center loss for cross-modality person Re-identification

Cited by 167 publications

References 37 publications

Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction

Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction

Circle-Based Ratio Loss for Person Reidentification

Dual-Path Deep Supervision Network with Self-Attention for Visible-Infrared Person Re-Identification

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