The exponential growth of streaming multi-modal data presents critical challenges for cross-modal retrieval: distribution shifts, modality gap, and scarce labels. Semi-supervised online cross-modal hashing has gained increasing interest due to its ability to encode complex streaming data and update hash functions simultaneously. Nevertheless, existing methods can hardly generate high-quality unsupervised hash codes, which fundamentally limits diversity and flexibility during the retrieval process. To this end, we propose a novel method named Prototype Evolution Online Cross-modal Hashing (PEOCH). By driving prototype evolution with semi-supervised streaming data, precise and stable hash codes are generated for both labeled and unlabeled data. Specifically, two prototype updates with stability guarantee are conducted: labeled samples push semantic knowledge into the supervised prototypes, while unlabeled samples perform clustering to generate unsupervised prototypes. Simultaneously, a co-optimization mechanism is designed to ensure the prototypes continuously evolve and preserve the consistency of the entire streaming data. Besides, an elasticity regularizer integrates discriminability and smoothness constraints, improving the reliability of prototypes. Extensive experiments on three benchmark datasets demonstrate that PEOCH outperforms state-of-the-art methods, achieving an average improvement of 6.7% in mAP@all across various retrieval tasks.

Online cross-modal hashing has gained increasing interest due to its ability to encode streaming data and update hash functions simultaneously. Existing online methods often assume either fully supervised or completely unsupervised settings. However, they overlook the prevalent and challenging scenario of semi-supervised cross-modal streaming data, where diverse data types, including labeled/unlabeled, paired/unpaired, and multi-modal, are intertwined. To address this issue, we propose Semi-Supervised Online Cross-modal Hashing (SSOCH). It presents an alignment-free pseudo-labeling strategy that extracts semantic information from unlabeled streaming data without relying on pairing relations. Furthermore, we design an online tri-consistent preserving scheme, integrating pseudo-labeled data regularization, discriminative label embedding, and fine-grained similarity preservation. This scheme fully explores consistency across data annotation, modalities, and streaming chunks, improving the model's adaptiveness in these challenging scenarios. Extensive experiments on benchmark datasets demonstrate the superiority of SSOCH under various scenarios, highlighting the importance of semi-supervised learning for online cross-modal hashing.

Semi-supervised hashing has shown promising efficacy in large-scale image retrieval, which learns similarity-preserving codes from both labeled and unlabeled data. To enable the use of advanced supervised hashing techniques, pseudo labels are widely applied. However, existing methods typically suffer from a biased learning issue due to pseudo label noise, which can be further aggravated during optimization. Although such a bias can adversely affect hashing accuracy, it has not been investigated sufficiently. In view of this, we present a comprehensive discussion on potential causes of biases, involving processes of pseudo-labeling, hash learning and optimization. Accordingly, a novel Generalized Debiased Semi-supervised Hashing (GDSH) method is proposed as a unified solution to mitigate the biases. Specifically, reliable pseudo labels are first predicted via a robust label completion strategy. Secondly, a debiased hash learning module is designed by combining label denoising and similarity updating. This can not only refine the supervision, but also obtain hash codes that are semantically debiased in both category and sample levels. Finally, a discrete semi-supervised hashing algorithm is proposed to alleviate the bias arising from optimization. Experimental results on three single-label and three multi-label image benchmarks demonstrate that GDSH remarkably outperforms the state-of-the-arts in different semi-supervised settings.

Multi-view clustering is valued for uncovering latent common semantics lying in multi-view data, which has been a hot topic in unsupervised learning. However, when dealing with incremental streaming views, existing approaches typically require reconstructing the view data and aggregating streaming representations, leading to misalignment between representation and clusters. More importantly, conducting the clustering process frequently results in significant time consumption. To address these issues, we propose a novel method called Binary Continual Stream-View Clustering (BCSVC). Specifically, we design a continual clustering method that seamlessly unifies streaming representation learning and cluster assignment within a single framework. We also introduce a variance-weighted center updating mechanism to smooth the frequent clustering operation and absorb the semantics of previous views. In addition, to reduce the time and space expenditure on computation and storage, binary code for clustering representations is introduced, which can also significantly improve the computational efficiency of continuous updates in streaming scenarios. Last but not least, comprehensive theoretical analysis and extensive experimental results demonstrate its superior performance under various scenarios.

With the advancement of remote sensing technology and the exponential growth of remote sensing visual data, efficiently retrieving remote sensing images from extensive databases has become increasingly important. Deep hashing, which combines the advantages of deep learning and hashing techniques, has emerged as a significant research direction in remote sensing image retrieval (RSIR). However, remote sensing images often contain substantial amounts of complex background information that are unrelated to the target. This noise can obscure or interfere with the target features, making it challenging for the model to effectively distinguish the target objects. To address these challenges, we propose a novel method called Supervised Discriminative Transformer Hashing (SDTH) for large-scale RSIR task, designed to enhance the retrieval of remote sensing images by extracting more distinguishable features. Our approach utilizes the Swin Transformer V2 architecture to improve feature extraction capabilities, thereby acquiring richer global contextual information and multi-scale features. To mitigate the impact of image noise and generate more discriminative hash codes, we propose to integrate batch-hard triplet loss with symmetric cross entropy loss. Experimental results on three benchmark datasets for remote sensing demonstrate the effectiveness and superiority of the proposed method.

Online hashing has emerged as a powerful tool for efficiently processing large-scale and streaming data. However, existing approaches often struggle with scalability limitations in similarity relations and inadequate discrimination provided by one-hot labels. To address these challenges, we propose Online Hashing with Discriminative Attribute Embedding (OHDAE). This novel method leverages a triple-matrix decomposition framework to dynamically decompose features into a dictionary, attributes, and category representations, effectively capturing semantic consistency without relying on accumulated data. To enhance the consistency and discriminability of attributes, we introduce an attribute construction strategy that integrates dictionary constraints with an online optimization strategy. Additionally, fine-grained semantic labels are embedded to improve the discriminability of hash codes by incorporating both semantic and similarity relationships. Experiments conducted on three benchmark datasets validate the superior performance, scalability, and robustness of OHDAE compared to existing state-of-the-art methods.

Unsupervised online cross-modal hashing has gained increasing attention for its effectiveness in streaming data retrieval. However, existing methods primarily focus on exploiting shared properties, overlooking semantic shifts among chunks and specific properties of each modality. To address these challenges, we propose a novel method called Unsupervised Online Cross-Modal Hashing with multiple association exploitation, UOCMH in short. Specifically, we design a hierarchical matrix factorization framework. It skillfully constructs robust orthogonal bases, multi-modality specific representations, and unified common representations, thereby capturing semantic associations among multi-modality streaming data more sufficiently. Additionally, we present a semantic auto-encoder scheme as hash functions. It builds the association between features and hash codes, facilitating the stability of the hashing process. Extensive experiments on the widely-used benchmark datasets demonstrate the superiority of the proposed UOCMH.

Multi-view clustering has achieved remarkable efficacy in integrating multi-view information, and received much research interest. Although anchor-based clustering algorithms have been well-investigated in past years, the separation of graph construction and category partitioning, can lead to suboptimal clustering performance and learning efficiency. To address these challenges, we propose a novel fast clustering algorithm named FAST-BAG. The proposed method can integrate the anchor graph construction and clustering partitioning seamlessly, breaking the separation between data fusion and task processes. Specifically, the multi-view data is unified into a consistent binary anchor graph with linear time complexity. Additionally, we leverage the high efficiency of binary distance computation to expedite the category partitioning process. Experiments conducted on five benchmark datasets validate the effectiveness and efficiency of the proposed method.

Unpaired cross-modal hashing which requires no supervision is a promising candidate to support large-scale retrieval across heterogeneous data. However, existing works focus on recovering pairwise relationships, which are usually time-consuming and sensitive to outliers. To tackle this issue, we propose a novel method termed Completely Unpaired Crossmodal Hashing (CUCH), which is applicable to scenarios where neither pairwise correspondence nor label information is available. The proposed CUCH creatively combines the merits of subspace recovery and cross-modal hashing, producing an effective subspace with both robustness and high efficiency. It first discovers robust subspace from each modality by excluding outliers. Then latent space translation is elaborated to obtain coupled subspace, based on which intermodal similarities can be captured. Moreover, the similarity-preservation property for CUCH is guaranteed. By manipulating subspaces rather than pairwise relations, CUCH reduces computational cost significantly. Experimental results demonstrate its advantages in various settings.

The spread of COVID-19 has brought great disaster to the world, and automatic segmentation of the infected region can help doctors make a quick diagnosis and reduce workload. However, automatic segmentation of infected regions in computed tomography (CT) images still faces many challenges. The most central problem at the moment is that when capturing low-frequency signals and high-frequency signals, the use of Transformer blocks for capturing low-frequency signals may deteriorate the high-frequency signals (local texture information) to some extent. For this reason, in this paper, we propose a new network for automatic segmentation of COVID-19 lung infections from CT images, called NextSeg. NextSeg follows an encoder-decoder architecture, where the encoder part uses NCB and NTB convolutional attention modules to extract better features while reducing the deterioration of texture profile information. The decoder part uses an attention fusion module to learn the weight relationship between features and retain more important feature information. In addition to the difficulty of extracting the boundary details of the COVID-19 infected region as well as the loss of details the deeper the network is, we designed a new hybrid strategy. Traditional and state-of-the-art approaches to the COVID-19 segmentation domain are compared. Extensive experiments show that our proposed framework outperforms existing competitors in both qualitative and quantitative results.

Unsupervised cross-modal hashing has received increasing attention because of its efficiency and scalability for large-scale data retrieval and analysis. However, existing unsupervised cross-modal hashing methods primarily focus on learning shared feature embedding, ignoring robustness and consistency across different modalities. To this end, this study proposes a novel method called scalable unsupervised hashing (SUH) for large-scale cross-modal retrieval. In the proposed method, latent semantic information and common semantic embedding within heterogeneous data are simultaneously exploited using multimodal clustering and collective matrix factorization, respectively. Furthermore, the robust norm is seamlessly integrated into the two processes, making SUH insensitive to outliers. Based on the robust consistency exploited from the latent semantic information and feature embedding, hash codes can be learned discretely to avoid cumulative quantitation loss. The experimental results on five benchmark datasets demonstrate the effectiveness of the proposed method under various scenarios.

Online cross-modal hashing has received increasing research attention due to its capability of encoding streaming data and updating hash functions simultaneously. Despite significant progress, there is still room for further improving accuracy from two aspects,i.e., 1) enhancing discrimination of hash codes with an efficient training process; 2) elevating generalization performance by harmonizing the training and retrieval process. Inspired by this, we propose an Online Discriminative Cross-modal Hashing method, called ODCH. To enlarge the inter-class margin and magnify the intra-class similarity, ODCH skillfully constructs a discriminative semantic space and seamlessly integrates bit balance and uncorrelation constraints, discrete optimization, and asymmetric strategy for embedding the discriminative semantic information into hamming space. Furthermore, ODCH attempts to boost the generalization process by bridging the gap between learning and generalization. It develops adaptive bit-wise weights to reflect different learning conditions among bits and transmits them into the generalization process. Besides, the proposed discriminative embedding and adaptive weighting can be adopted by existing supervised cross-modal hashing methods, achieving more precise performance than the original versions. Extensive experiments on three benchmarked datasets show that ODCH achieves up to an average of 4.17% mAP score gains compared to state-of-the-art online cross-modal hashing methods, indicating its superiority.

Large-scale cross-modal hashing has drawn extensive attention due to its attractive efficiency in both storage and retrieval. Existing methods exhibit poor performance when exploiting the semantic correlations implied in unsupervised and unpaired data during training process. To deal with this issue, we propose a novel hashing method, named Semi-supervised Semi-paired Cross-modal Hashing (SSCH). By leveraging a general and flexible two-step scheme, the proposed method can handle the complex training data effectively and efficiently, where both the common semantics and the modality-specific optimal pseudo semantics are well captured. Specifically, the proposed SSCH performs an alignment-free pseudo-labeling process to get strengthened semantic information. Furthermore, hash representations for various data are learned via a label-enhanced strategy, through which the cross-modal correlations are strengthened and preserved with considering efficiency. The semantic-preserving proof of SSCH is given based on statistical analysis. Also, we prove the stability of the proposed time-saving algorithm using properties of Bregman divergence. Experimental results on three benchmark datasets show that SSCH can obtain satisfactory precision and scalability in various scenarios.

Unsupervised hashing has attracted extensive attention in effectively and efficiently tackling large-scale cross-modal retrieval task. Existing methods typically try to mine the latent common subspace across multimodal data without any category annotation. Despite the exciting progress, there are still three challenges that need to be further addressed: (1) efficiently improving the robustness during latent common subspace learning; (2) harmoniously embedding the intra-modal inherence and inter-modal relevance of multimodal data into Hamming space; and (3) effectively reducing the training time complexity and making the model scalable for large-scale datasets. To well address the above challenges, this study proposes a method named Fast Unsupervised Cross-Modal Hashing (FUCH). Specifically, FUCH proposes a semantic-aware collective matrix factorization to learn robust representation via exploiting latent category-specific attributes, and introduces Cauchy loss to measure the factorization process. Accordingly, the above process can effectively embed potential discriminative information into common space, while making the model insensitive for outliers. Moreover, FUCH designs a dual projection learning scheme, which not only learns modality-unique hash functions to excavate individual properties but also learns modality-mutual hash functions to multimodal correlational properties. Experimental results on three benchmark datasets verify the effectiveness of FUCH under various scenarios.