# Dynamic Enhancement Network for Partial Multi-modality Person Re-identification arXiv: https://arxiv.org/abs/2305.15762 ยท HTML: https://mlsanity.com/paper/2305.15762/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 54 references that are in the map. - First posted on arXiv 25 May 2023. ## Builds on (most cited first, 30 of 32) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020) - [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019) - [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018) - [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017) - [Learning Factorized Multimodal Representations](https://mlsanity.com/paper/1806.06176/) (2018) - [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019) - [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016) - [Relation-Aware Global Attention for Person Re-identification](https://mlsanity.com/paper/1904.02998/) (2019) - [Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification](https://mlsanity.com/paper/2108.08728/) (2021) - [Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2007.09314/) (2020) - [HeMIS: Hetero-Modal Image Segmentation](https://mlsanity.com/paper/1607.05194/) (2016) - [RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment](https://mlsanity.com/paper/1910.05839/) (2019) - [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network](https://mlsanity.com/paper/1802.09655/) (2018) - [Cross-modality Person re-identification with Shared-Specific Feature Transfer](https://mlsanity.com/paper/2002.12489/) (2020) - [Multi-Level Factorisation Net for Person Re-Identification](https://mlsanity.com/paper/1803.09132/) (2018) - [Deep Cross-Modal Hashing](https://mlsanity.com/paper/1602.02255/) (2016) - [VRSTC: Occlusion-Free Video Person Re-Identification](https://mlsanity.com/paper/1907.08427/) (2019) - [Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks](https://mlsanity.com/paper/1502.07209/) (2015) - [FSSD: Feature Fusion Single Shot Multibox Detector](https://mlsanity.com/paper/1712.00960/) (2017) - [Brain Tumor Segmentation on MRI with Missing Modalities](https://mlsanity.com/paper/1904.07290/) (2019) - [Medical Image Fusion: A survey of the state of the art](https://mlsanity.com/paper/1401.0166/) (2013) - [Hetero-Center Loss for Cross-Modality Person Re-Identification](https://mlsanity.com/paper/1910.09830/) (2019) - [GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval](https://mlsanity.com/paper/1709.04329/) (2017) - [Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification](https://mlsanity.com/paper/1912.01300/) (2019) ## Built on by (most cited first, 10 of 10) - [Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2403.10254/) (2024) - [IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2503.10324/) (2025) - [MDReID: Modality-Decoupled Learning for Any-to-Any Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2510.23301/) (2025) - [DMPT: Decoupled Modality-aware Prompt Tuning for Multi-modal Object Re-identification](https://mlsanity.com/paper/2504.10985/) (2025) - [Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning](https://mlsanity.com/paper/2504.14847/) (2025) - [FUSE: Frequency-domain Unification and Spectral Energy Alignment for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2606.20044/) (2026) - [Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification](https://mlsanity.com/paper/2607.14821/) (2026) - [Multi-Modal Object Re-Identification with Prompt-S6 and Semantic-Aware Knowledge Guidance](https://mlsanity.com/paper/2607.23451/) (2026) - [Multi-Modal Object Re-Identification with Dual Semantic Guidance and Global-Local Mutual Modulation](https://mlsanity.com/paper/2607.29207/) (2026) - [Incentive Noise and Structural Prior Infusion for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2609.24539/) (2026) ## Abstract (arXiv) Many existing multi-modality studies are based on the assumption of modality integrity. However, the problem of missing arbitrary modalities is very common in real life, and this problem is less studied, but actually important in the task of multi-modality person re-identification (Re-ID). To this end, we design a novel dynamic enhancement network (DENet), which allows missing arbitrary modalities while maintaining the representation ability of multiple modalities, for partial multi-modality person Re-ID. To be specific, the multi-modal representation of the RGB, near-infrared (NIR) and thermal-infrared (TIR) images is learned by three branches, in which the information of missing modalities is recovered by the feature transformation module. Since the missing state might be changeable, we design a dynamic enhancement module, which dynamically enhances modality features according to the missing state in an adaptive manner, to improve the multi-modality representation. Extensive experiments on multi-modality person Re-ID dataset RGBNT201 and vehicle Re-ID dataset RGBNT100 comparing to the state-of-the-art methods verify the effectiveness of our method in complex and changeable environments.