Semi-Supervised Knowledge Distillation for Cross-Modal Hashing

Hashing has been extensively utilized in cross-modal retrieval due to its high efficiency in handling large-scale, high-dimensional data. However, most existing cross-modal hashing methods operate as offline learning models, which learn hash codes in a batch-based manner and prove to be inefficient for streaming data. Recently, several online cross-modal hashing methods have been proposed to address the streaming data scenario. Nevertheless, these methods fail to fully leverage the semantic information and accurately optimize hashing in a discrete fashion. As a result, both the accuracy and efficiency of online cross-modal hashing methods are not ideal. To address these issues, this paper introduces the Semantic Embedding-based Online Cross-modal Hashing (SEOCH) method, which integrates semantic information exploitation and online learning into a unified framework. To exploit the semantic information, we map the semantic labels to a latent semantic space and construct a semantic similarity matrix to preserve the similarity between new data and existing data in the Hamming space. Moreover, we employ a discrete optimization strategy to enhance the efficiency of cross-modal retrieval for online hashing. Through extensive experiments on two publicly available multi-label datasets, we demonstrate the superiority of the SEOCH method.

Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several long-tail hashing methods have been proposed but they can not adapt for multi-modal data, due to the complex interplay between labels and individuality and commonality information of multi-modal data. Furthermore, CMH methods mostly mine the commonality of multi-modal data to learn hash codes, which may override tail labels encoded by the individuality of respective modalities. In this paper, we propose LtCMH (Long-tail CMH) to handle imbalanced multi-modal data. LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities. Then it dynamically combines the individuality and commonality with direct features extracted from respective modalities to create meta features that enrich the representation of tail labels, and binaries meta features to generate hash codes. LtCMH significantly outperforms state-of-the-art baselines on long-tail datasets and holds a better (or comparable) performance on datasets with balanced labels.

Multi-label enhancement based self-supervised deep cross-modal hashing

Due to the fast retrieval speed and low storage cost, cross-modal hashing has become the primary method for cross-modal retrieval. Since the emergence of deep cross-modal hashing methods, cross-modal retrieval significantly improved. However, the existing cross-modal hash retrieval methods still need to effectively utilize the dataset’s supervisory information and the lack of similarity expression ability. This means that the label information needs to be maximized, and the potential semantic relationship between two modalities cannot be fully explored, thus affecting the judgment of semantic similarity between two modalities. To address these problems, this paper proposes Tri-CMH, a cross-modal hash retrieval method with fused triples, which is an end-to-end modeling framework consisting of two parts: feature extraction and hash learning. Firstly, the multi-modal data are preprocessing into the form of triple groups. The data supervision matrix is constructed so that the samples with labels and their meanings are aggregated together. In contrast, the samples with labels and their opposite meanings are separated, thus avoiding the problem of the under-utilization of supervisory information in the data set and achieving the effect of efficiently utilizing the global supervisory information. Meanwhile, the loss function of the hash learning part is optimized by considering the Hamming distance loss, single-modality internal loss, cross-modality loss, and quantization loss to explicitly constrain semantically similar hash codes and semantically dissimilar hash codes and to improve the model’s ability to judge cross-modality semantic similarity. The method is trained and tested on the IAPR-TC12, MIRFLICKR-25K, and NUS-WIDE datasets, and the experimental evaluation criteria are mAP and PR curve, and the experimental results show the effectiveness and practicality of the method.

Cross-modal hashing (CMH) maps heterogeneous multiple modality data into compact binary code to achieve fast and flexible retrieval across different modalities, especially in large-scale retrieval. As the data don't need a lot of manual annotation, unsupervised cross-modal hashing has a wider application prospect than supervised method. However, the existing unsupervised methods are difficult to achieve satisfactory performance due to the lack of credible supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised Knowledge Distillation Cross-Modal Hashing method (KDCMH), which can use similarity information distilled from unsupervised method to guide supervised method. Specifically, firstly, the teacher model adopted an unsupervised distribution-based similarity hashing method, which can construct a modal fusion similarity matrix.Secondly, under the supervision of teacher model distillation information, student model can generate more discriminative hash codes. In two public datasets NUS-WIDE and MIRFLICKR-25K, extensive experiments have proved the significant improvement of KDCMH on several representative unsupervised cross-modal hashing methods.

Cross-modal hashing encodes heterogeneous multimedia data into compact binary code to achieve fast and flexible retrieval across different modalities. Due to its low storage cost and high retrieval efficiency, it has received widespread attention. Supervised deep hashing significantly improves search performance and usually yields more accurate results, but requires a lot of manual annotation of the data. In contrast, unsupervised deep hashing is difficult to achieve satisfactory performance due to the lack of reliable supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised knowledge distillation cross-modal hashing method based on semantic alignment (SAKDH), which can reconstruct the similarity matrix using the hidden correlation information of the pretrained unsupervised teacher model, and the reconstructed similarity matrix can be used to guide the supervised student model. Specifically, firstly, the teacher model adopted an unsupervised semantic alignment hashing method, which can construct a modal fusion similarity matrix. Secondly, under the supervision of teacher model distillation information, the student model can generate more discriminative hash codes. Experimental results on two extensive benchmark datasets (MIRFLICKR-25K and NUS-WIDE) show that compared to several representative unsupervised cross-modal hashing methods, the mean average precision (MAP) of our proposed method has achieved a significant improvement. It fully reflects its effectiveness in large-scale cross-modal data retrieval.

Discriminative correlation hashing for supervised cross-modal retrieval

Cross-modal hashing has received widespread attentions on cross-modal retrieval task due to its superior retrieval efficiency and low storage cost. However, most existing cross-modal hashing methods learn binary codes directly from multimedia data, which cannot fully utilize the semantic knowledge of the data. Furthermore, they cannot learn the ranking based similarity relevance of data points with multi-label. And they usually use a relax constraint of hash code which causes non-negligible quantization loss in the optimization. In this paper, a hashing method called Deep Adversarial Discrete Hashing (DADH) is proposed to address these issues for cross-modal retrieval. The proposed method uses adversarial training to learn features across modalities and ensure the distribution consistency of feature representations across modalities. We also introduce a weighted cosine triplet constraint which can make full use of semantic knowledge from the multi-label to ensure the precise ranking relevance of item pairs. In addition, we use a discrete hashing strategy to learn the discrete binary codes without relaxation, by which the semantic knowledge from label in the hash codes can be preserved while the quantization loss can be minimized. Ablation experiments and comparison experiments on two cross-modal databases show that the proposed DADH improves the performance and outperforms several state-of-the-art hashing methods for cross-modal retrieval.

Hashing has recently received great attention in cross-modal retrieval. Cross-modal retrieval aims at retrieving information across heterogeneous modalities (e.g., texts vs. images). Cross-modal hashing compresses heterogeneous high-dimensional data into compact binary codes with similarity preserving, which provides efficiency and facility in both retrieval and storage. In this study, we propose a novel fast discrete cross-modal hashing (FDCH) method with regressing from semantic labels to take advantage of supervised labels to improve retrieval performance. In contrast to existing methods that learn the projection from hash codes to semantic labels, the proposed FDCH regresses the semantic labels of training examples to the corresponding hash codes with a drift. It not only accelerates the hash learning process, but also helps generate stable hash codes. Furthermore, the drift can adjust the regression and enhance the discriminative capability of hash codes. Especially in the case of training efficiency, FDCH is much faster than existing methods. Comparisons with several state-of-the-art techniques on three benchmark datasets have demonstrated the superiority of FDCH under various cross-modal retrieval scenarios.

Cross-modal hashing methods have attracted considerable attention. Most pioneer approaches only preserve the neighborhood relationship by constructing the correlations among heterogeneous modalities. However, they neglect the fact that the high-dimensional data often exists on a low-dimensional manifold embedded in the ambient space and the relative proximity between the neighbors is also important. Although some methods leverage the manifold learning to generate the hash codes, most of them fail to explicitly explore the discriminative information in the class labels and discard the binary constraints during optimization, generating large quantization errors. To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH). It can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information. Moreover, the hash codes can be generated discretely by an iterative optimization algorithm, which can avoid the large quantization errors. Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods.

Label consistent locally linear embedding based cross-modal hashing

Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, most existing CMH methods are based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure. As a result, existing CMH methods with hand-crafted features may not achieve satisfactory performance. In this paper, we propose a novel CMH method, called deep cross-modal hashing (DCMH), by integrating feature learning and hash-code learning intothe same framework. DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch. Experiments on three real datasets with image-text modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications.

Cross-modal hashing maps heterogeneous multimedia data into a common Hamming space to realize fast and flexible cross-modal retrieval. Supervised cross-modal hashing methods have achieved considerable progress by incorporating semantic side information. However, they heavily rely on large-scale labeled cross-modal training data which are hard to obtain, since multiple modalities are involved. They also ignore the rich information contained in the large amount of unlabeled data across different modalities, which can help to model the correlations between different modalities. To address these problems, in this paper, we propose a novel semi-supervised cross-modal hashing approach by generative adversarial network (SCH-GAN). The main contributions can be summarized as follows: 1) we propose a novel generative adversarial network for cross-modal hashing, in which the generative model tries to select margin examples of one modality from unlabeled data when given a query of another modality (e.g., giving a text query to retrieve images and vice versa). The discriminative model tries to distinguish the selected examples and true positive examples of the query. These two models play a minimax game so that the generative model can promote the hashing performance of the discriminative model and 2) we propose a reinforcement learning-based algorithm to drive the training of proposed SCH-GAN. The generative model takes the correlation score predicted by discriminative model as a reward, and tries to select the examples close to the margin to promote a discriminative model. Extensive experiments verify the effectiveness of our proposed approach, compared with nine state-of-the-art methods on three widely used datasets.

Recently, due to the low storage consumption and high search efficiency of hashing methods and the powerful feature extraction capability of deep neural networks, deep cross-modal hashing has received extensive attention in the field of multi-media retrieval. However, existing methods tend to ignore the latent relationships between heterogeneous data when learning a common semantic subspace, and cannot retain more important semantic information when mining deep correlations. In this paper, an attention mechanism which focuses on the characteristics of the associated features is employed to propose an attention-aware semantic fusion matrix that integrates important information from different modalities. We introduce a novel network that can pass the extracted features through the attention module to efficiently encode rich and relevant features, and can also generate hash codes under the self-supervision of the proposed attention-aware semantic fusion matrix. Our experimental results and detailed analysis prove that our method can achieve better retrieval performance on the three popular datasets, compared with the recent unsupervised cross-modal hashing methods.

With the increasing amount of multimedia data, cross-modality hashing has made great progress as it achieves sub-linear search time and low memory space. However, due to the huge discrepancy between different modalities, most existing cross-modality hashing methods cannot learn unified hash codes and functions for modalities at the same time. The gap between separated hash codes and functions further leads to bad search performance. In this paper, to address the issues above, we propose a novel end-to-end Deep Unified Cross-Modality Hashing method named DUCMH, which is able to jointly learn unified hash codes and unified hash functions by alternate learning and data alignment. Specifically, to reduce the discrepancy between image and text modalities, DUCMH utilizes data alignment to learn an auxiliary image to text mapping under the supervision of image-text pairs. For text data, hash codes can be obtained by unified hash functions, while for image data, DUCMH first maps images to texts by the auxiliary mapping, and then uses the mapped texts to obtain hash codes. DUCMH utilizes alternate learning to update unified hash codes and functions. Extensive experiments on three representative image-text datasets demonstrate the superiority of our DUCMH over several state-of-the-art cross-modality hashing methods.