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
Due to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. However, it is still challenging to produce compact and discriminative hash codes for... Show moreDue to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. However, it is still challenging to produce compact and discriminative hash codes for images asso-ciated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing methods are based upon an oversimplified similarity assignment (i.e., 0 for instance pairs sharing no label, 1 for instance pairs sharing at least 1 label), 2) the exploration in multi-semantic relevance are insufficient or even neglected in many of the existing methods. These problems significantly limit the dis-crimination of generated hash codes. In this paper, we propose a novel Deep Hashing with Self-Supervised Asymmetric Semantic Excavation and Margin-Scalable Constraint(SADH) approach to cope with these problems. SADH implements a self-supervised network to sufficiently preserve semantic information in a semantic feature dictionary and a semantic code dictionary for the semantics of the given dataset, which efficiently and precisely guides a feature learning network to preserve multi-label semantic information using an asymmetric learning strategy. By further exploiting semantic dictionaries, a new margin-scalable constraint is employed for both precise similarity searching and robust hash code generation. Extensive empirical research on four popular benchmarks validates the proposed method and shows it outperforms several state-of-the-art approaches. The source codes URL of our SADH is: http:// github.com/SWU-CS-MediaLab/SADH. (c) 2022 Elsevier B.V. All rights reserved. Show less
The recent deep cross-modal hashing (DCMH) has achieved superior performance in effective and efficient cross-modal retrieval and thus has drawn increasing attention. Nevertheless, there are still... Show moreThe recent deep cross-modal hashing (DCMH) has achieved superior performance in effective and efficient cross-modal retrieval and thus has drawn increasing attention. Nevertheless, there are still two limitations for most existing DCMH methods: (1) single labels are usually leveraged to measure the semantic similarity of cross-modal pairwise instances while neglecting that many cross-modal datasets contain abundant semantic information among multi-labels. (2) several DCMH methods utilized the multi-labels to supervise the learning of hash functions. Nevertheless, the feature space of multilabels suffers the weakness of sparse, resulting in sub-optimization for the hash functions learning. Thus, this paper proposed a multi-label modality enhanced attention-based self-supervised deep cross-modal hashing (MMACH) framework. Specifically, a multi-label modality enhanced attention module is designed to integrate the significant features from cross-modal data into multi-labels feature representations, aiming to improve its completion. Moreover, a multi-label cross-modal triplet loss is defined based on the criterion that the feature representations of cross-modal pairwise instances with more common categories should preserve higher semantic similarity than other instances. To the best of our knowledge, the multi-label cross-modal triplet loss is the first time designed for cross-modal retrieval. Extensive experiments on four multi-label cross-modal datasets demonstrate the effectiveness and efficiency of our proposed MMACH. Moreover, the MMACH also achieved superior performance and outperformed several state-of-the-art methods on the task of cross-modal retrieval. The source code of MMACH is available at https://github.com/SWU-CS-MediaLab/MMACH. (c) 2021 Elsevier B.V. All rights reserved. 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
Accurately matching visual and textual data in cross-modal retrieval has been widely studied in the multimedia community. To address these challenges posited by the heterogeneity gap and the... Show moreAccurately matching visual and textual data in cross-modal retrieval has been widely studied in the multimedia community. To address these challenges posited by the heterogeneity gap and the semantic gap, we propose integrating Shannon information theory and adversarial learning. In terms of the heterogeneity gap, we integrate modality classification and information entropy maximization adversarially. For this purpose, a modality classifier (as a discriminator) is built to distinguish the text and image modalities according to their different statistical properties. This discriminator uses its output probabilities to compute Shannon information entropy, which measures the uncertainty of the modality classification it performs. Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy. Thus, maximizing information entropy gradually reduces the distribution discrepancy of cross-modal features, thereby achieving a domain confusion state where the discriminator cannot classify two modalities confidently. To reduce the semantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss are used to associate the intra- and inter-modality similarity between features in the shared space. Furthermore, a regularization term based on KL-divergence with temperature scaling is used to calibrate the biased label classifier caused by the data imbalance issue. Extensive experiments with four deep models on four benchmarks are conducted to demonstrate the effectiveness of the proposed approach. Show less
In recent years a vast amount of visual content has been generated and shared from various fields, such as social media platforms, medical images, and robotics. This abundance of content creation... Show moreIn recent years a vast amount of visual content has been generated and shared from various fields, such as social media platforms, medical images, and robotics. This abundance of content creation and sharing has introduced new challenges. In particular, searching databases for similar content, i.e.content based image retrieval (CBIR), is a long-established research area, and more efficient and accurate methods are needed for real time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of intelligent search. In this survey we organize and review recent CBIR works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers. We identify and present the commonly-used benchmarks and evaluation methods used in the field. We collect common challenges and propose promising future directions. More specifically, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, aiming to promote a global view of the field of instance-based CBIR. Show less
Visible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. Except for the intra-modality variance that RGB-RGB person... Show moreVisible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. Except for the intra-modality variance that RGB-RGB person reidentification mainly overcomes, VI-ReID suffers from additional inter-modality variance caused by the inherent heterogeneous gap. To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modalityfeature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively. Disentangling cross-modality identity-discriminable features leads to more robust retrieval for VI-ReID. To achieve efficient optimization like conventional VAE, we theoretically derive two variational inference terms for the MoG prior under the supervised setting, which not only restricts the identity-discriminable subspace so that the model explicitly handles the cross-modality intra-identity variance, but also enables the MoG distribution to avoid posterior collapse. Furthermore, we propose a triplet swap reconstruction (TSR) strategy to promote the above disentangling process. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two VI-ReID datasets. Codes will be available at https://github.com/TPCD/DG-VAE. Show less
In numerous multimedia and multi-modal tasks from image and video retrieval to zero-shot recognition to multimedia question and answering, bridging image and text representations plays an... Show moreIn numerous multimedia and multi-modal tasks from image and video retrieval to zero-shot recognition to multimedia question and answering, bridging image and text representations plays an important and in some cases an indispensable role. To narrow the modality gap between vision and language, prior approaches attempt to discover their correlated semantics in a common feature space. However, these approaches omit the intra-modal semantic consistency when learning the inter-modal correlations. To address this problem, we propose cycle-consistent embeddings in a deep neural network for matching visual and textual representations. Our approach named as CycleMatch can maintain both inter-modal correlations and intra-modal consistency by cascading dual mappings and reconstructed mappings in a cyclic fashion. Moreover, in order to achieve a robust inference, we propose to employ two late-fusion approaches: average fusion and adaptive fusion. Both of them can effectively integrate the matching scores of different embedding features, without increasing the network complexity and training time. In the experiments on cross-modal retrieval, we demonstrate comprehensive results to verify the effectiveness of the proposed approach. Our approach achieves state-of-the-art performance on two well-known multi-modal datasets, Flickr30K and MSCOCO. Show less
