Deep hashing offers efficient storage and fast retrieval capabilities. As a result, it has been extensively applied to large‑scale retrieval tasks. To alleviate the dependence on high-quality annotated data, recent research has focused on unsupervised domain adaptive hashing methods, which aim to transfer knowledge from a label-rich source domain to a label-scarce target domain. However, in open-world scenarios, source domain labels are often inevitably noisy, which tends to undermine the quality of learned hash codes and induce considerable performance deterioration. To this end, we introduce a novel Robust Domain Adaptive Hashing (RDAH) method to jointly mitigate the adverse effects of label noise and domain discrepancy. Specifically, we first model the loss distribution of training samples using a two-component Gaussian mixture model to estimate each sample’s confidence, based on which the data is partitioned. Subsequently, we introduce a neighbor consistency-guided correction strategy, which leverages the semantic structure of high-confidence neighbors to perform weighted correction on noisy samples. Moreover, we design a dual-level cross-domain alignment mechanism that jointly mitigates domain shift from two complementary perspectives. Extensive experimental results validate the effectiveness and robustness of RDAH across multiple benchmark datasets.

Domain-adaptive hashing enhances discriminative hash representations by transferring knowledge from a label-rich source domain to a label-scarce target domain. It has attracted significant attention due to its ability to enable efficient cross-domain retrieval without requiring target domain labels. However, existing methods generally assume that source domain labels are completely accurate. In practice, labels obtained via web crawling or crowdsourcing often contain varying degrees of noise, which hampers semantic alignment and aggravates domain shift. To tackle these issues, we propose a novel method termed Evidential Learning and Progressive Alignment (ELPA) for domain-adaptive hashing. This method comprises two key modules: the Uncertainty-aware Noise Separation (UNS) and the Progressive Cross-domain Alignment (PCA). In the UNS, we exploit the belief and uncertainty masses obtained from the evidential learning model and utilize the posterior probabilities of a Gaussian Mixture Model to effectively distinguish clean samples from noisy ones. In PCA, we introduce a progressive partial optimal transport mechanism that prioritizes pseudo-label generation for well-aligned target samples, thereby gradually achieving class-level and global-level cross-domain alignment. Extensive experiments across multiple benchmark datasets with various noise ratios demonstrate that ELPA consistently surpasses existing state-of-the-art methods, exhibiting superior robustness and generalization capability.

Supervised cross-modal retrieval (CMR) achieves excellent performance thanks to the semantic information provided by its labels, which helps to establish semantic correlations between samples from different modalities. However, in real-world scenarios, there often exists a large amount of unlabeled and unpaired multimodal training data, rendering existing methods unfeasible. To address this issue, we propose a novel partially aligned cross-modal retrieval method called Optimal Transport-based Prototype Alignment Learning (OTPAL). Due to the high computational complexity involved in directly establishing matching correlations between unannotated unaligned cross-modal samples, instead, we establish matching correlations between shared prototypes and samples. To be specific, we employ the optimal transport algorithm to establish cross-modal alignment information between samples and prototypes, and then minimize the distance between samples and their corresponding prototypes through a specially designed prototype alignment loss. As an extension of this paper, we also extensively investigate the influence of incomplete multimodal data on cross-modal retrieval performance under the partially aligned setting proposed above. To further address the above more challenging scenario, we raise a scalable prototype-based neighbor feature completion method, which better captures the correlations between incomplete samples and neighbor samples through a cross-modal self-attention mechanism. Experimental results on four benchmark datasets show that our method can obtain satisfactory accuracy and scalability in various real-world scenarios.

Semi-supervised cross-modal retrieval (SS-CMR) aims at learning modality invariance and semantic discrimination from labeled data and unlabeled data, which is crucial for practical applications in the real-world. The key to essentially addressing the SS-CMR task is to solve the semantic association and modality heterogeneity problems. To address these issues, in this paper, we propose a novel semi-supervised cross-modal retrieval method, namely Semi-supervised Prototype Semantic Association Learning (SPAL) for robust cross-modal retrieval. To be specific, we employ shared semantic prototypes to associate labeled and unlabeled data over both modalities to minimize intra-class and maximize inter-class variations, thereby improving discriminative representations on unlabeled data. What is more important is that we propose a novel pseudo-label guided contrastive learning to refine cross-modal representation consistency in the common space, which leverages pseudo-label semantic graph information to constrain cross-modal consistent representations. Meanwhile, multi-modal data inevitably suffers from the cost and difficulty of data collection, resulting in the incomplete multimodal data problem. Thus, to strengthen the robustness of the SS-CMR, we propose a novel prototype propagation method for incomplete data to reconstruct completion representations which preserves the semantic consistency. Extensive evaluations using several baseline methods across four benchmark datasets demonstrate the effectiveness of our method.

A long line of research on secure computation has confirmed that anything that can be computed, can be computed securely using a set of non-colluding parties. Indeed, this non-collusion assumption makes a number of problems solvable, as well as reduces overheads and bypasses computational hardness results, and it is pervasive across different privacy-enhancing technologies. However, it remains highly susceptible to covert, undetectable collusion among computing parties. This work stems from an observation that if the number of available computing parties is much higher than the number of parties required to perform a secure computation task, collusion attempts in privacy-preserving computations could be deterred.We focus on the prominent privacy-preserving computation task of multi-server 1-private information retrieval (PIR) that inherently assumes no pair-wise collusion. For PIR application scenarios, such as those for blockchain light clients, where the available servers can be plentiful, a single server’s deviating action is not tremendously beneficial to itself. We can make deviations undesired via small amounts of rewards and penalties, thus significantly raising the bar for collusion resistance. We design and implement a collusion mitigation mechanism on a public bulletin board with payment execution functions, considering only rational and malicious parties with no honest non-colluding servers. Privacy protection is offered for an extended period after the query executions.

Domain generalization in person re-identification (ReID) aims to design a generalizable model, which is trained under the supervision of a set of labeled source domains and can be directly deployed on unknown domains. Existing approaches simply treat each identity as a distinct class and ignore the differences among cameras. We argue that the camera information is crucial for learning discriminative representations, as people’s behavior usually varies between cameras. In this paper, we present Multi-Centroid Memory (MCM) to capture different camera information for each identity and Soft Triple Hard (ST-Hard) loss to align the information of the same identity across cameras. Furthermore, in contrast to the traditional approaches of training a single model using a parallel training mechanism, we propose the Recurrent Implicit Lifelong Learning (RILL) that feeds the source domains into the model in a continuous loop to train an expert for each domain. To make each expert further generalized to other source domains, during the training on the current domain, RILL adopts a style replay-based method to simulate the training of the previous domain, encouraging each domain’s expert to extract generalizable features. We also present Earth Mover’s Test-time Adaption (EMTA) to be used in conjunction with RILL, which enables source domains that are more similar to the test domain to play a more significant role in the test. This is achieved by our proposed Earth Mover’s Similarity (EMS), which helps model the similarities between the source and test domains. Extensive experiments on two evaluation protocols fully demonstrate our framework’s generalization and competitiveness.

Automated Market Makers (AMMs) are decentralized applications that allow users to exchange crypto-tokens without the need for a matching exchange order. AMMs are one of the most successful DeFi use cases: indeed, major AMM platforms process a daily volume of transactions worth USD billions. Despite their popularity, AMMs are well-known to suffer from transaction-ordering issues: adversaries can influence the ordering of user transactions, and possibly front-run them with their own, to extract value from AMMs, to the detriment of users. We devise an effective procedure to construct a strategy through which an adversary can maximize the value extracted from user transactions.

Cross-modal retrieval often faces the challenges of eliminating modality gap, learning robust modality invariance and semantic discrimination. Existing self-supervised crossmodal approaches still suffer from the faulty negative sample selection strategy and the lack of reliable high-level semantic discriminative guidance. Therefore, we propose a robust self-supervised co-training instance and semantic discrimination learning method (RCL) for cross-modal retrieval. Specifically, by the k-reciprocal nearest neighbor to generate pairwise pseudo-labels, we can correctly select negative samples and better filter the false negative ones, and thus pull semantically similar instances closer in a similar supervised contrastive learning. In addition, we use prototype contrastive learning to learn high-level semantic discriminative representations from different semantic groups, which pull instances and prototype vectors closer to better learn the semantic structure of multimodal data. Extensive experiments demonstrate the effectiveness of our method on cross-modal datasets.

Domain Generalization in person re-identification (ReID) aims to learn a generalizable model from a single or multi-source domain that can be directly deployed to an unseen domain without fine-tuning. In this paper, we investigate the problem of single-source domain generalization in ReID. Recent research has gained remarkable progress by treating image matching as a search for local correspondences in feature maps. However, to ensure efficient matching, they usually adopt a pixel-wise matching approach, which is prone to be deviated by the identity-irrelevant patch features in the image, such as background patches. To address this problem, we propose the Multi-scale Query-Adaptive Convolution (QAConv-MS) framework. Specifically, we adopt a group of template kernels with different scales to extract local features of different receptive fields from the original feature maps and accordingly perform the local matching process. We also introduce a self-attention branch to extract global features from the feature map as complementary information for local features. Our approach achieves state-of-the-art performances on four large-scale datasets.

Domain generalization person re-identification (DG-ReID) aims to train a generalizable model over several source domains that can perform well on unseen target domains, which makes the DG-ReID task challenging since the model is not allowed to access any target data during training. The classic meta-learning method, which simulates the train-test process of DG-ReID task, is a popular and effective way for DG-ReID. Nevertheless, the method still suffers from the unstable meta-optimization problem. We thus propose a novel Dynamically Adaptive Instance Normalization and Attention-Aware Incremental Meta-Learning (DAIML) optimization method to effectively address this issue. In addition, most existing DG-ReID studies generally utilize Instance Normalization (IN) to learn domain-irrelevant features for eliminating domain shift, which may lead to the removal of effective domain-specific discriminative information that is usually useful to enhance the discriminability of a particular source. Therefore, we propose a dynamically adaptive IN (DAIN) that can balance well the learning of domain-invariant representations and domain-specific discriminative features. Furthermore, we apply channel attention and spatial attention to the proposed DAIN for further improving the domain-irrelevant discriminative information. Extensive experiments demonstrate the effectiveness of our proposed method on several DG-ReID datasets.

Traditional text-based person re-identification (ReID) techniques heavily rely on fully matched multi-modal data, which is an ideal scenario. However, due to inevitable data missing and corruption during the collection and processing of cross-modal data, the incomplete data issue is usually met in real-world applications. Therefore, we consider a more practical task termed the incomplete text-based ReID task, where person images and text descriptions are not completely matched and contain partially missing modality data. To this end, we propose a novel Prototype-guided Cross-modal Completion and Alignment (PCCA) framework to handle the aforementioned issues for incomplete text-based ReID. Specifically, we cannot directly retrieve person images based on a text query on missing modality data. Therefore, we propose the cross-modal nearest neighbor construction strategy for missing data by computing the cross-modal similarity between existing images and texts, which provides key guidance for the completion of missing modal features. Furthermore, to efficiently complete the missing modal features, we construct the relation graphs with the aforementioned cross-modal nearest neighbor sets of missing modal data and the corresponding prototypes, which can further enhance the generated missing modal features. Additionally, for tighter fine-grained alignment between images and texts, we raise a prototype-aware cross-modal alignment loss that can effectively reduce the modality heterogeneity gap for better fine-grained alignment in common space. Extensive experimental results on several benchmarks with different missing ratios amply demonstrate that our method can consistently outperform state-of-the-art text-image ReID approaches.

Domain generalization (DG) in person re-identification (ReID) is an extremely challenging but essential task, which aims to learn a generalizable model over multiple labeled source domains that can perform well on unseen target domains. Most existing DG strategies in ReID directly aggregate multiple source data together for training, incurring a large inter-domain bias and unstable model optimization that lead the model apt to overfitting domain bias and the model training more time-consuming respectively, thus hampering the generalization and convergence speed of the model. To tackle the aforementioned issues, inspired by Curriculum Learning that mimics the process of human lifelong DG learning (from easy to hard), we put forward a novel Debiased Contrastive Curriculum Learning (DCCL) strategy for DG ReID, which is designed to incrementally enhance generalization in an easy-to-hard training way that can continuously accumulate learning experience to make learning in unknown domains easier and effectively eliminate the domain bias to help the model learn rich domain-invariant discriminative features, thereby strengthening generalization and accelerating convergence for the model. In addition, to simultaneously learn class-level and instance-level discriminative representations, we raise a non-parametric hybrid contrastive loss to equip the DCCL model. We also particularly design an inter-domain mix module to variegate the features of the newly added source domain at each stage of DCCL, further establishing the advantages of DCCL. Extensive experimental results on four public ReID benchmarks fully demonstrate that our DCCL can effectively strengthen the generalization capacity of the model to unseen domains and outperform the state-of-the-art methods.

Domain generalization(DG) in person re-identification (ReID) attracts increasing attention due to its practical applications. It aims to learn a model that, after training on multiple source domains, can be applied directly to the unseen domains without further training. In order to develop a domain-robust model for unseen domains, we propose the Memory-based Anti-Hard-instances Domain-Mix(MAD) framework. Specifically, a memory-based non-parametric contrastive loss is adopted to replace the traditional parametric cross-entropy loss. To prevent overfitting on the source domains, we present an Anti-Hard-instances module to mitigate the effect of hard instances. We also introduce a Domain-Mix module to diversify the features in the source domains, further enhancing the generalization ability of our model. Extensive experiments on four large-scale ReID datasets fully demonstrate the strong generalization and competitiveness of our framework.

Cross-modal retrieval is an essential area of representation learning, which aims to retrieve instances with the same semantics from different modalities. In real implementation, a key challenge for cross-modal retrieval is to narrow the heterogeneity gap between different modalities and obtain modality-invariant and discriminative features. Typically, existing approaches for this task mainly learn inter-modal invariance and focus on how to combine pair-level loss and class-level loss, which cannot effectively and adequately learn discriminative features. To address these issues, in this paper, we propose a novel Cross-Modality Cross-Instance Contrastive Learning for Cross-Media Retrieval (C3CMR) method. Specifically, to fully employ the intra-modal similarities, we introduce the intra-modal contrastive learning to enhance the discriminative power of the unimodal features. Besides, we design a supervised inter-modal contrastive learning scheme to take full advantage of the label semantic associations. In this way, cross-semantic associations and inter-modal invariance can be further learned. Moreover, pertaining to the local suboptimal semantic similarity by only mining pairwise and triplewise sample relationships, we propose the cross-instance contrastive learning to mine the similarities among multiple instances. Comprehensive experimental results on four widely-used benchmark datasets demonstrate the superiority of our proposed method over several state-of-the-art cross-modal retrieval methods.

Repeated Modular Squaring is a versatile computational operation that has led to practical constructions of timed-cryptographic primitives like time-lock puzzles (TLP) and verifiable delay functions (VDF) that have a fast growing list of applications. While there is a huge interest for timed-cryptographic primitives in the blockchains area, we find two real-world concerns that need immediate attention towards their large-scale practical adoption: Firstly, the requirement to constantly perform computations seems unrealistic for most of the users. Secondly, choosing the parameters for the bound (T) seems complicated due to the lack of heuristics and experience. We present OpenSquare, a decentralized repeated modular squaring service, that overcomes the above concerns. OpenSquare lets clients outsource their repeated modular squaring computation via smart contracts to any computationally powerful servers that offer computational services for rewards in an unlinkable manner.