Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification

By Jianlou Si, Honggang Zhang, Chun-Guang Li, Jason Kuen, Xiangfei Kong, Alex ChiChung Kot, G. Wang. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

486 citations recorded by the source corpus.

Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space. However, the methods based on a single feature vector are not sufficient enough to overcome visual ambiguity, which frequently occurs in real scenario. In this paper, we propose a novel end-to-end trainable framework, called Dual ATtention Matching network (DuATM), to learn context-aware feature sequences and perform attentive sequence comparison simultaneously. The core component of our DuATM framework is a dual attention mechanism, in which both intrasequence and inter-sequence attention strategies are used for feature refinement and feature-pair alignment, respectively. Thus, detailed visual cues contained in the intermediate feature sequences can be automatically exploited and properly compared. We train the proposed DuATM network as a siamese network via a triplet loss assisted with a decorrelation loss and a cross-entropy loss. We conduct extensive experiments on both image and video based ReID benchmark datasets. Experimental results demonstrate the significant advantages of our approach compared to the state-of-the-art methods.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7daa2c0f76fd3bfc7feadf313d6ac7504d4ecd20

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Citations in the corpus

Deep Learning for Person Re-Identification: A Survey and Outlook (2020, methodology): “We review the deeply learned Re-ID models, including CoSeg [135], GLTR [140], STA [139], ADFD [108], STC [19], DRSA [136], Snippet [138], ETAP [128], DuATM [85], SDM [182], TwoS [130], ASTPN [134], RQEN [178], Forest [133], RNN [129] reported. mAP values (”
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification (2021, unknown): “Another group of methods [32, 48, 26, 8] adopts attention mechanism on video-based person re-identification task to discover key parts in videos.”
Bilinear Attention Networks for Person Retrieval (2019, background): “Much effort has gone into studying and addressing these issues [20, 36, 38, 32, 23, 41, 17, 22, 35]; however, it still remains a dominant problem and calls for further study.”
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification (2020, methodology): “Attention Mechanisms have been widely used in various applications to enhance the feature representation [37,42,53,3].”
ABD-Net: Attentive but Diverse Person Re-Identification (2019, background): “Since then, the attention-based models [8, 9, 10, 11, 12] have boosted person Re-ID performance much.”
Adaptive Transfer Network for Cross-Domain Person Re-Identification (2019, background): “Deep learning based methods [47, 14, 26, 15, 40] for feature extraction have shown substantial advantage over traditional hard-crafted features on most of person reidentification datasets.”
Relation-Aware Global Attention for Person Re-Identification (2019, background): “In comparison with the attention based approaches [28, 25, 39, 19] which leverage human semantics ( e.g . foreground/background, human part segmentation) and those [22, 27, 32] which learn attention from input images themselves, our RGA-SC signiﬁcantly outperforms them.”
Omni-Scale Feature Learning for Person Re-Identification (2019, methodology): “In recent years, deep convolutional neural networks (CNNs) have been widely used in person ReID to learn discriminative features [2, 26, 28, 38, 43, 57, 58, 69].”
A Strong Baseline and Batch Normalization Neck for Deep Person Re-Identification (2019, methodology): “Attention-based methods [25]–[28] involve an attention mechanism to extract additional discriminative features.”
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training (2018, methodology): “Inspired by attention models, in [16, 14, 21], the attention-based deep neural networks are proposed to capture multiple attentions and select multi-scale attentive features.”
Identity-Guided Human Semantic Parsing for Person Re-Identification (2020, background): “Attention mechanism constructs alignment by suppressing background noise and enhancing the discriminative regions [3, 23, 31, 41, 50, 58].”
Memory-Based Neighbourhood Embedding for Visual Recognition (2019, unknown): “0 DuATM [29] CVPR’18 64.”
Bag of Tricks and a Strong Baseline for Deep Person Re-Identification (2019, unknown): “8 DuATM [14] 1 91.”