Generalizable Person Re-Identification by Domain-Invariant Mapping Network
We aim to learn a domain generalizable person re-identification (ReID) model. When such a model is trained on a set of source domains (ReID datasets collected from different camera networks), it can be directly applied to any new unseen dataset for effective ReID without any model updating. Despite its practical value in real-world deployments, generalizable ReID has seldom been studied. In this work, a novel deep ReID model termed Domain-Invariant Mapping Network (DIMN) is proposed. DIMN is designed to learn a mapping between a person image and its identity classifier, i.e., it produces a classifier using a single shot. To make the model domain-invariant, we follow a meta-learning pipeline and sample a subset of source domain training tasks during each training episode. However, the model is significantly different from conventional meta-learning methods in that: (1) no model updating is required for the target domain, (2) different training tasks share a memory bank for maintaining both scalability and discrimination ability, and (3) it can be used to match an arbitrary number of identities in a target domain. Extensive experiments on a newly proposed large-scale ReID domain generalization benchmark show that our DIMN significantly outperforms alternative domain generalization or meta-learning methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain GeneralizationGeneralizable Person Re-identificationMeta-LearningPerson Re-IdentificationSimilar Papers 제목 키워드 기반
Learning Domain Invariant Representations for Generalizable Person Re-Identification
Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community. In this work, we construct a structural causal model among identity labels, identity-specific factors (cl…
Data AugmentationDomain GeneralizationGeneralizable Person Re-identificationPerson Re-Identification+1DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations
Existing person re-identification models often have low generalizability, which is mostly due to limited availability of large-scale labeled data in training. However, labeling large-scale training data is very expensive…
Domain AdaptationGeneralizable Person Re-identificationPerson Re-IdentificationUnsupervised Domain AdaptationTAL: Two-stream Adaptive Learning for Generalizable Person Re-identification
Domain generalizable person re-identification aims to apply a trained model to unseen domains. Prior works either combine the data in all the training domains to capture domain-invariant features, or adopt a mixture of e…
Domain GeneralizationGeneralizable Person Re-identificationMixture-of-ExpertsPerson Re-Identification+1CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification
The Visual Language Model, known for its robust cross-modal capabilities, has been extensively applied in various computer vision tasks. In this paper, we explore the use of CLIP (Contrastive Language-Image Pretraining),…
Generalizable Person Re-identificationLanguage ModelingLanguage ModellingPerson Re-IdentificationDual Distribution Alignment Network for Generalizable Person Re-Identification
Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the ta…
Domain GeneralizationGeneralizable Person Re-identificationPerson Re-Identification