We are living in an information era where the amount of image and video data increases exponentially. It is important to develop intelligent visual understanding systems to satisfy our need for... Show moreWe are living in an information era where the amount of image and video data increases exponentially. It is important to develop intelligent visual understanding systems to satisfy our need for searching information of interest. An important example of such a system that, with the current increasing concern for public security, is urgently required, is an automated person Re-Identification (ReID) system. This thesis mainly focuses on exploring ReID systems via deep learning methods. To enable ReID systems to meet the so-called open-world challenges, we explore three themes that are challenging yet practical in real application scenarios: lifelong learning, unsupervised domain adaptation and cross-modality challenge. Furthermore, this thesis provides numerous experiments and in-depth analysis, which can help motivate further research on the three research themes. Show less
Lifelong person re-identification (LReID) is a challenging and emerging task, which concerns the ReID capability on both seen and unseen domains after learning across different domains continually.... Show moreLifelong person re-identification (LReID) is a challenging and emerging task, which concerns the ReID capability on both seen and unseen domains after learning across different domains continually. Existing works on LReID are devoted to introducing commonlyused lifelong learning approaches, while neglecting a serious side effect caused by using normalization layers in the context of domainincremental learning. In this work, we aim to raise awareness of the importance of training proper batch normalization layers by proposing a new meta reconciliation normalization (MRN) method specifically designed for tackling LReID. Our MRN consists of grouped mixture standardization and additive rectified rescaling components, which are able to automatically maintain an optimal balance between domain-dependent and domain-independent statistics, and even adapt MRN for different testing instances. Furthermore, inspired by synaptic plasticity in human brain, we present a MRNbased meta-learning framework for mining the meta-knowledge shared across different domains, even without replaying any previous data, and further improve the model’s LReID ability with theoretical analyses. Our method achieves new state-of-the-art performances on both balanced and imbalanced LReID benchmarks. Show less
Person re-identification (ReID) methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are... Show morePerson re-identification (ReID) methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are ineffective because the domain is continually changing in which case incremental learning over multiple domains is required potentially. In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains. Following the cognitive processes in the human brain, we design an Adaptive Knowledge Accumulation (AKA) framework that is endowed with two crucial abilities: knowledge representation and knowledge operation. Our method alleviates catastrophic forgetting on seen domains and demonstrates the ability to generalize to unseen domains. Correspondingly, we also provide a new and large-scale benchmark for LReID. Extensive experiments demonstrate our method outperforms other competitors by a margin of 5.8% mAP in generalising evaluation. The codes will be available at https: //github.com/TPCD/LifelongReID. Show less
