In recent years, hashing-based online cross-modal retrieval has garnered growing attention. This trend is motivated by the fact that web data is increasingly delivered in a streaming manner as opposed to batch processing. Simultaneously, the sheer scale of web data sometimes makes it impractical to fully load for the training of hashing models. Despite the evolution of online cross-modal hashing techniques, several challenges remain: 1) Most existing methods learn hash codes by considering the relevance among newly arriving data or between new data and the existing data, often disregarding valuable global semantic information. 2) A common but limiting assumption in many methods is that the label space remains constant, implying that all class labels should be provided within the first data chunk. This assumption does not hold in real-world scenarios, and the presence of new labels in incoming data chunks can severely degrade or even break these methods.

Text recognition reads from real scene text or handwritten text, facilitating many real-world applications such as driverless cars, visual Q&A, and image-based machine translation. Although impressive results have been achieved in single-domain text recognition, it still suffers from great challenges in cross-domain due to the domain gaps among the synthetic text, the real scene text, and the handwritten text. Existing standard unsupervised domain adaptation (UDA) methods struggle to solve the text recognition task since they view a domain or a text image (containing a character sequence) as a whole, ignoring the subunits that make up the sequence. In the paper, we present a Prototyped-based Unsupervised Domain Adaptation method for text recognition (ProtoUDA), where the class prototypes are computed from the source domain, target domain, and the mixed (source-target) domain, respectively. Technically, ProtoUDA initially extracts pseudo-labeled character features under word-level supervised information. Further, based on these character features, we propose two parallel and complementary modules to perform class-level and instance-level alignment, which explicitly transfer the knowledge learned in the source domain to the target domain. Among them, class-level alignment is to close the distance between the similar source prototypes and target prototypes. The instance-level alignment is based on contrastive learning, making the character instances of the mixed domain close to the corresponding class mixed prototype while staying away from other class mixed prototypes. To our knowledge, we are the first to adopt contrastive learning in UDA-based text recognition tasks. Extensive experiments on several benchmark datasets show the superiority of our method over state-of-the-art methods.

The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their micro-videos, which not only provides them with business income but helps the audiences to discover their interesting products. However, due to the micro-video recording by unprofessional equipment, involving various topics and including multiple modalities, it is challenging to locate the products related to micro-videos efficiently, appropriately, and accurately. We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances.

Breast cancer is the most common type of cancers in women. Therefore, how to accurately and timely diagnose it becomes very important. Some computer-aided diagnosis models based on pathological images have been proposed for this task. However, there are still some issues that need to be further addressed. For example, most deep learning based models suffer from a lack of interpretability. In addition, some of them cannot fully exploit the information in medical data, e.g., hierarchical label structure and scattered distribution of target objects. To address these issues, we propose a weakly supervised fine-grained medical image classification method for breast cancer diagnosis, i.e., DLD-Net for short. It simulates the diagnostic procedures of pathologists by multiple attention-guided cropping and dropping operations, making it have good clinical interpretability. Moreover, it cannot only exploit the global information of a whole image, but also further mine the critical local information by generating and selecting critical regions from the image. In light of this, those subtle discriminating information hidden in scattered regions can be exploited. In addition, we also design a novel hierarchical cross-entropy loss to utilize the hierarchical label information in medical images, making the classification results more discriminative. Furthermore, DLD-Net is a weakly supervised network, which can be trained end-to-end without any additional region annotations. Extensive experimental results on three benchmark datasets demonstrate that DLD-Net is able to achieve good results and outperforms some state-of-the-art methods.

With the rapid development of social media, various types of tags uploaded by social users are attached to the images. Compared to clean labels marked by experts, although user-provided tags are imperfect, e.g., wrong tags, reduplicative tags, or missing tags, they are more diverse, fine-grained, and informative. Currently, there exist several weakly-supervised hashing methods attempting to learn hash codes using tags as supervision. Although they could benefiting from the rich information contained in tags, most of them may defy the nature of social media data. In real scenarios, social media data appears in streaming fashion, but most weakly-supervised hashing methods are just batch-based which cannot effectively handle streaming data. To this end, only one weakly-supervised online hashing method has been proposed, but it is still far from enough to alleviate the negative effects of tags.

Despite great success achieved, deep learning methods are vulnerable to noise in the training dataset, including adversarial perturbations and annotation noise. These harmful factors significantly influence the learning process of deep models, leading to less confident models. However, existing methods have not yet studied this practical and challenging issue.

Hashing methods for cross-modal retrieval have recently been widely investigated due to the explosive growth of multimedia data. Generally, real-world data is imperfect and has more or less redundancy, making cross-modal retrieval task challenging. However, most existing cross-modal hashing methods fail to deal with the redundancy, leading to unsatisfactory performance on such data. In this paper, to address this issue, we propose a novel cross-modal hashing method, namely aTtEntion-Aware deep Cross-modal Hashing (TEACH). It could perform feature learning and hash-code learning simultaneously. Besides, with designed attention modules for different modalities, one for each, TEACH can effectively highlight the useful information of data while suppressing the redundant information. Extensive experiments on benchmark datasets demonstrate that our method outperforms some state-of-the-art hashing methods in cross-modal retrieval tasks.

The hashing technique has recently sparked much attention in information retrieval community due to its high efficiency in terms of storage and query processing. For cross-modal retrieval tasks, existing supervised hashing models either treat the semantic labels as the ground truth and formalize the problem to a classification task, or further add a similarity matrix as supervisory signals to pursue hash codes of high quality to represent coupled data. However, these approaches are incapable of ensuring that the learnt binary codes preserve well the semantics and similarity relationships contained in the supervised information. Moreover, for sophisticated discrete optimization problems, it is always addressed by continuous relaxation or bit-wise solver, which leads to a large quantization error and inefficient computation. To relieve these issues, in this paper, we present a two-step supervised discrete hashing method, i.e., laBel ReconstrUcting and Similarity preserving Hashing (BRUSH). We formulate it as an asymmetric pairwise similarity-preserving problem by using two latent semantic embeddings deducted from decomposing semantics and reconstructing semantics, respectively. Meanwhile, the unified binary codes are jointly generated based on both embeddings with the affinity guarantee, such that the discriminative property of the obtained hash codes can be significantly enhanced alongside preserving semantics well. In addition, by adopting two-step hash learning strategy, our method simplifies the procedure of the hashing function and binary codes learning, thus improving the flexibility and efficiency. The resulting discrete optimization problem is also elegantly solved by the proposed alternating algorithm without any relaxation. Extensive experiments on benchmarks demonstrate that BRUSH outperforms the state-of-the-art methods, in terms of efficiency and effectiveness.

Recently, with the discoveries in neurobiology, high-dimensional sparse hashing has attracted increasing attention. In contrast with general hashing that generates low-dimensional hash codes, the high-dimensional sparse hashing maps inputs into a higher dimensional space and generates sparse hash codes, achieving superior performance. However, the sparse hashing has not been fully studied in hashing literature yet. For example, how to fully explore the power of sparse coding in cross-modal retrieval tasks; how to discretely solve the binary and sparse constraints so as to avoid the quantization error problem. Motivated by these issues, in this paper, we present an efficient sparse hashing method, i.e., High-dimensional Sparse Cross-modal Hashing, HSCH for short. It not only takes the high-level semantic similarity of data into consideration, but also properly exploits the low-level feature similarity. In specific, we theoretically design a fine-grained similarity with two critical fusion rules. Then we take advantage of sparse codes to embed the fine-grained similarity into the to-be-learnt hash codes. Moreover, an efficient discrete optimization algorithm is proposed to solve the binary and sparse constraints, reducing the quantization error. In light of this, it becomes much more trainable, and the learnt hash codes are more discriminative. More importantly, the retrieval complexity of HSCH is as efficient as general hash methods. Extensive experiments on three widely-used datasets demonstrate the superior performance of HSCH compared with several state-of-the-art cross-modal hashing approaches.

Supervised cross-modal hashing has attracted much attention. However, there are still some challenges, e.g., how to effectively embed the label information into binary codes, how to avoid using a large similarity matrix and make a model scalable to large-scale datasets, how to efficiently solve the binary optimization problem. To address these challenges, in this paper, we present a novel supervised cross-modal hashing method, i.e., scalaBle Asymmetric discreTe Cross-modal Hashing, BATCH for short. It leverages collective matrix factorization to learn a common latent space for the labels and different modalities, and embeds the labels into binary codes by minimizing a distance-distance difference problem. Furthermore, it builds a connection between the common latent space and the hash codes by an asymmetric strategy. In the light of this, it can perform cross-modal retrieval and embed more similarity information into the binary codes. In addition, it introduces a quantization minimization term and orthogonal constraints into the optimization problem, and generates the binary codes discretely. Therefore, the quantization error and redundancy may be much reduced. Moreover, it is a two-step method, making the optimization simple and scalable to large-scale datasets. Extensive experimental results on three benchmark datasets demonstrate that BATCH outperforms some state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency.

Recently, cross-modal hashing (CMH) methods have attracted much attention. Many methods have been explored; however, there are still some issues that need to be further considered. 1) How to efficiently construct the correlations among heterogeneous modalities. 2) How to solve the NP-hard optimization problem and avoid the large quantization errors generated by relaxation. 3) How to handle the complex and difficult problem in most CMH methods that simultaneously learning the hash codes and hash functions. To address these challenges, we present a novel cross-modal hashing algorithm, named Asymmetric Discrete Cross-Modal Hashing (ADCH). Specifically, it leverages the collective matrix factorization technique to learn the common latent representations while preserving not only the cross-correlation from different modalities but also the semantic similarity. Instead of relaxing the binary constraints, it generates the hash codes directly using an iterative optimization algorithm proposed in this work. Based the learnt hash codes, ADCH further learns a series of binary classifiers as hash functions, which is flexible and effective. Extensive experiments are conducted on three real-world datasets. The results demonstrate that ADCH outperforms several state-of-the-art cross-modal hashing baselines.

Hashing technique has attracted considerable attention for large-scale multimedia retrieval due to its low storage cost and fast query speed. Moreover, many hashing models have been proposed for cross-modal retrieval task. However, there are still some problems that need to be further considered. For example, a majority of them directly use linear projection matrix to project heterogeneous data into a common space, which may lead to large error as there are some heterogeneous data with semantic similarity hard to be close in latent space when linear projection is used. Besides, most existing cross-modal hashing methods use a simple pairwise similarity matrix for preserving the label information when learning. This kind of pairwise similarity cannot fully utilize the discriminative property of label information. Furthermore, most existing supervised ones try to solve a relaxed continuous optimization problem by dropping the discrete constraints, which may lead to large quantization error. To overcome these limitations, in this paper, we propose a novel cross-modal hashing method, called Dictionary Learning based Supervised Discrete Hashing (DLSDH). Specifically, it learns dictionaries and generates sparse representation for every instance, which is more suitable to be projected to a latent space. To make full use of label information, it uses cosine similarity to construct a new pairwise similarity matrix which can contain more information. Moreover, it directly learns the discrete hash codes instead of relaxing the discrete constraints. Extensive experiments are conducted on three benchmark datasets and the results demonstrate that it outperforms several state-of-the-art methods for cross-modal retrieval task.

Despite significant progress in supervised hashing, there are three common limitations of existing methods. First, most pioneer methods discretely learn hash codes bit by bit, making the learning procedure rather time-consuming. Second, to reduce the large complexity of the n by n pairwise similarity matrix, most methods apply sampling strategies during training, which inevitably results in information loss and suboptimal performance; some recent methods try to replace the large matrix with a smaller one, but the size is still large. Third, among the methods that leverage the pairwise similarity matrix, most of them only encode the semantic label information in learning the hash codes, failing to fully capture the characteristics of data. In this paper, we present a novel supervised hashing method, called Fast Scalable Supervised Hashing (FSSH), which circumvents the use of the large similarity matrix by introducing a pre-computed intermediate term whose size is independent with the size of training data. Moreover, FSSH can learn the hash codes with not only the semantic information but also the features of data. Extensive experiments on three widely used datasets demonstrate its superiority over several state-of-the-art methods in both accuracy and scalability. Our experiment codes are available at: https://lcbwlx.wixsite.com/fssh.

Supervised hashing methods have attracted much attention in these years. However, most existing supervised hashing algorithms have some of the following problems. First, most of them leverage the pairwise similarity matrix, whose size is quadratic to the number of training samples, to supervise the learning of hash codes. Thus, they are not scalable when dealing with large data. Second, most of them relax the discrete constraints for easy optimization and then quantize the learnt real-valued solution to binary hash codes. Therefore, the quantization error caused by the relaxation may lead to a decline of retrieval performance. To address these issues and make the supervised method scalable to large datasets, we present a novel hashing method, named Scalable Supervised Discrete Hashing (SSDH). Specifically, based on a new loss function, SSDH bypasses the direct optimization on the n by n pairwise similarity matrix. In addition, SSDH adopts no relaxation optimization scheme in the learning procedure and avoids the large quantization error problem. Moreover, during learning, it leverages both the pairwise similarity matrix and label matrix; thus, more semantic information can be embedded to the learning of hash codes. Extensive experiments are conducted on six benchmark datasets including two large-scale datasets, i.e., NUS-WIDE and ImageNet. The results show that SSDH can outperform state-of-the-art baselines on these datasets, demonstrating its effectiveness and efficiency.

Finger vein image retrieval is one significant technique for performing fast identification especially in large-scale applications. However, most existing retrieval methods were based on fixed-scale feature of non-overlapped rectangular image block, in which the representation ability of feature and the local consistency of vein pattern were both overlooked. And the weak encoding (e.g., predefined threshold based binarization) was also limited the retrieval performance. Focusing on these problems, this paper proposes a novel finger vein image retrieval framework based on similarity-preserving encoding of scale-varied superpixel feature. In the framework, locally consistent pixels in one superpixel are used as a unit of feature representation, and the feature length is varied with the category of the superpixel classified by the variance of lowest dimensional feature. Additionally, the feature compaction and feature rotation based encoding can minimize the quantization loss and preserve the similarity between the scale-varied feature and the encoded binary codes. Experimental results on six public finger vein databases demonstrate that the superiority of the proposed coding scale-varied superpixel feature based retrieval approach over the state-of-the-arts.