In recent years, numerous hashing techniques have been developed to boost efficient cross-modal retrieval. Once a retrieval model is deployed, the hash code length is fixed to achieve optimal performance. To address different retrieval scenarios while maintaining retrieval accuracy, a common approach is to redesign and retrain the original model with different hash code length. However, this retraining process can increase the training load and may lead to worse results. To tackle these challenges, we present Regenerated Cross-Modal Hashing (RCMH), a novel cross-modal hashing framework designed to improve the quality of existing hash codes and convert them to arbitrary lengths with high efficiency. First, we clip or pad the existing hash codes to initialize them with the target length, under the supervision of the similarity matrix generated by the augmented label information. Second, we introduce a linear-nonlinear competitive reconstruction approach to reduce the semantic gaps and further capture the deeper relationships from linear image features and nonlinear text features. In this way, each pair of samples is compared and selected to obtain reconstructed binary codes that can preserve the modality-specific properties. Finally, to reduce the training costs caused by iterations of variables, the regenerate hashing term is utilized to regenerate final hash codes with the reconstructed binary codes while preserving the information from the existing hash codes without iterative optimization. Notably, RCMH can be integrated with existing state-of-the-art (SOTA) methods with robustness, helping them to adjust the hash code length and achieve better retrieval performance.

Deep online cross-modal hashing has gained much attention from researchers recently, as its promising applications with low storage requirement, fast retrieval efficiency and cross modality adaptive, etc. However, there still exists some technical hurdles that hinder its applications, e.g., 1) how to extract the coexistent semantic relevance of cross-modal data, 2) how to achieve competitive performance when handling the real time data streams, 3) how to transfer the knowledge learned from offline to online training in a lightweight manner. To address these problems, this paper proposes a lightweight contrastive distilled hashing (LCDH) for cross-modal retrieval, by innovatively bridging the offline and online cross-modal hashing by similarity matrix approximation in a knowledge distillation framework. Specifically, in the teacher network, LCDH first extracts the cross-modal features by CLIP, which are further fed into an attention module for representation enhancement after feature fusion. Then, the output of the attention module is fed into a FC layer to obtain hash codes for aligning the sizes of similarity matrices for online and offline training. In the student network, LCDH extracts the visual and textual features by lightweight models, and then the features are fed into a FC layer to generate binary codes. Finally, by approximating the similarity matrices, the performance of online hashing in the lightweight student network can be enhanced by the supervision of coexistent semantic relevance that is distilled from the teacher network. Experimental results on three widely used datasets demonstrate that LCDH outperforms some state-of-the-art methods.

Supervised cross-modal hashing has achieved remarkable progress in retrieving related items across different modalities. However, in practical applications, a significant portion of data remains unlabeled, such as online data on websites, which must be included for effective retrieval. To address this challenge, while maintaining the high accuracy and efficiency of supervised methods, few works have attempted to adapt existing supervised techniques to handle unsupervised tasks through a general modular approach. To this end, we introduce a novel cross-modal hashing method, termed Label Prediction Inherited Hashing (LPIH). Initially, LPIH leverages labeled data to learn high-quality general label functions using supervised methods. Subsequently, it inherits the existing hash codes from existing supervised methods to further refine the pseudo-label information. Finally, LPIH integrates the refined pseudo-label information with the existing hash functions to learn new hash functions specifically tailored for unsupervised tasks. Extensive experimental results on three public datasets demonstrate the superior performance of LPIH compared to state-of-the-art (SOTA) cross-modal hashing methods. Specifically, LPIH achieves an average precision improvement of 5% over SOTA methods, highlighting its effectiveness in bridging the gap between supervised and unsupervised learning in the context of cross-modal retrieval.

Modern programming languages are constantly evolving, introducing new language features and APIs to enhance software development practices. Software developers often face the tedious task of upgrading their codebase to new programming language versions. Recently, large language models (LLMs) have demonstrated potential in automating various code generation and editing tasks, suggesting their applicability in automating code upgrade. However, there exists no benchmark for evaluating the code upgrade ability of LLMs, as distilling code changes related to programming language evolution from real-world software repositories’ commit histories is a complex challenge. In this work, we introduce CoUpJava, the first large-scale dataset for code upgrade, focusing on the code changes related to the evolution of Java. CoUpJava comprises 10,697 code upgrade samples, distilled from the commit histories of 1,379 open-source Java repositories and covering Java versions 7–23. The dataset is divided into two subsets: CoUpJava-FINE, which captures fine-grained method-level refactorings towards new language features; and CoUpJava-COARSE, which includes coarse-grained repository-level changes encompassing new language features, standard library APIs, and build configurations. Our proposed dataset provides high-quality samples by filtering irrelevant and noisy changes and verifying the compilability of upgraded code. Moreover, CoUpJava reveals diversity in code upgrade scenarios, ranging from small, fine-grained refactorings to large-scale repository modifications.

Cross-modal retrieval is a promising technique nowadays to find semantically similar instances in other modalities while a query instance is given from one modality. However, there still exists many challenges for reducing heterogeneous modality gap by embedding label information to discrete hash codes effectively, solving the binary optimization when generating unified hash codes and reducing the discrepancy of data distribution efficiently during common space learning. In order to overcome the above-mentioned challenges, we propose a Collaboratively Semantic alignment and Metric learning for cross-modal Hashing (CSMH) in this paper. Specifically, by a kernelization operation, CSMH first extracts the non-linear data features for each modality, which are projected into a latent subspace to align both marginal and conditional distributions simultaneously. Then, a maximum mean discrepancy-based metric strategy is customized to mitigate the distribution discrepancies among features from different modalities. Finally, semantic information obtained from the label similarity matrix, is further incorporated to embed the latent semantic structure into the discriminant subspace. Experimental results of CSMH and baseline methods on four widely-used datasets show that CSMH outperforms some state-of-the-art hashing baseline methods for cross-modal retrieval on efficiency and precision.

In the past decades, supervised cross-modal hashing methods have attracted considerable attentions due to their high searching efficiency on large-scale multimedia databases. Many of these methods leverage semantic correlations among heterogeneous modalities by constructing a similarity matrix or building a common semantic space with the collective matrix factorization method. However, the similarity matrix may sacrifice the scalability and cannot preserve more semantic information into hash codes in the existing methods. Meanwhile, the matrix factorization methods cannot embed the main modality-specific information into hash codes. To address these issues, we propose a novel supervised cross-modal hashing method called random online hashing (ROH) in this article. ROH proposes a linear bridging strategy to simplify the pair-wise similarities factorization problem into a linear optimization one. Specifically, a bridging matrix is introduced to establish a bidirectional linear relation between hash codes and labels, which preserves more semantic similarities into hash codes and significantly reduces the semantic distances between hash codes of samples with similar labels. Additionally, a novel maximum eigenvalue direction (MED) embedding method is proposed to identify the direction of maximum eigenvalue for the original features and preserve critical information into modality-specific hash codes. Eventually, to handle real-time data dynamically, an online structure is adopted to solve the problem of dealing with new arrival data chunks without considering pairwise constraints. Extensive experimental results on three benchmark datasets demonstrate that the proposed ROH outperforms several state-of-the-art cross-modal hashing methods.

Cross-modal retrieval has attracted considerable attention for searching in large-scale multimedia databases because of its efficiency and effectiveness. As a powerful tool of data analysis, matrix factorization is commonly used to learn hash codes for cross-modal retrieval, but there are still many shortcomings. First, most of these methods only focus on preserving locality of data but they ignore other factors such as preserving reconstruction residual of data during matrix factorization. Second, the energy loss of data is not considered when the data of cross-modal are projected into a common semantic space. Third, the data of cross-modal are directly projected into a unified semantic space which is not reasonable since the data from different modalities have different properties. This article proposes a novel method called average approximate hashing (AAH) to address these problems by: 1) integrating the locality and residual preservation into a graph embedding framework by using the label information; 2) projecting data from different modalities into different semantic spaces and then making the two spaces approximate to each other so that a unified hash code can be obtained; and 3) introducing a principal component analysis (PCA)-like projection matrix into the graph embedding framework to guarantee that the projected data can preserve the main energy of data. AAH obtains the final hash codes by using an average approximate strategy, that is, using the mean of projected data of different modalities as the hash codes. Experiments on standard databases show that the proposed AAH outperforms several state-of-the-art cross-modal hashing methods.