Deep cross-modal hashing with fine-grained similarity

Multi-label enhancement based self-supervised deep 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 has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications.

Due to the continuous growth of large-scale multi-modal data and increasing requirements for retrieval speed, deep cross-modal hashing has gained increasing attention recently. Most of existing studies take a similarity matrix as supervision to optimize their models, and the inner product between continuous surrogates of hash codes is utilized to depict the similarity in the Hamming space. However, all of them merely consider the relevant information to build the similarity matrix, ignoring the contribution of the irrelevant one, i.e., the categories that samples do not belong to. Therefore, they cannot effectively alleviate the effect of dissimilar samples. Moreover, due to the modality distribution difference, directly utilizing continuous surrogates of hash codes to calculate similarity may induce suboptimal retrieval performance. To tackle these issues, in this paper, we propose a novel deep normalized cross-modal hashing scheme with bi-direction relation reasoning, named Bi_NCMH. Specifically, we build the multi-level semantic similarity matrix by considering bi-direction relation, i.e., consistent and inconsistent relation. It hence can holistically characterize relations among instances. Besides, we execute feature normalization on continuous surrogates of hash codes to eliminate the deviation caused by modality gap, which further reduces the negative impact of binarization on retrieval performance. Extensive experiments on two cross-modal benchmark datasets demonstrate the superiority of our model over several state-of-the-art baselines.

Deep cross-modal hashing has become an essential tool for supervised multimodal search. These models tend to be optimized with large, curated multimodal datasets, where most labels have been manually verified. Unfortunately, in many scenarios, such accurate labeling may not be avail-able. In contrast, datasets with low-quality annotations may be acquired, which inevitably introduce numerous mis-takes or label noise and therefore degrade the search per-formance. To address the challenge, we present a general robust cross-modal hashing framework to correlate distinct modalities and combat noisy labels simultaneously. More specifically, we propose a proxy-based contrastive (PC) loss to mitigate the gap between different modalities and train networks for different modalities Jointly with small-loss samples that are selected with the PC loss and a mu-tual quantization loss. The small-loss sample selection from such Joint loss can help choose confident examples to guide the model training, and the mutual quantization loss can maximize the agreement between different modalities and is beneficial to improve the effectiveness of sample selection. Experiments on three widely-used multimodal datasets show that our method significantly outperforms existing state-of-the-arts.

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.

MS[formula omitted]GAH: Multi-label semantic supervised graph attention hashing for robust cross-modal retrieval

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.

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.

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) aims to bridge the semantic gap between heterogeneous modalities by learning compact binary representations for efficient retrieval. Most existing deep cross-modal hashing methods are developed under the assumption that multimodal data are complete and perfectly paired across modalities. However, this assumption rarely holds as real-world multimodal datasets often suffer from missing modalities due to inconsistencies, imbalances, or noise during data collection. To address such incomplete data, existing incomplete CMH methods typically attempt to reconstruct the missing information by exploiting internal signals from the available modalities. Nonetheless, these internally guided completion strategies tend to be highly sensitive to distributional shifts, leading to substantial performance degradation on unseen or out-of-distribution data. Inspired by the human learning mechanism of enhancing cognition through external knowledge, this paper proposes a novel External Guidance Incomplete Cross-modal Hashing (EGICH) framework to address this limitation. Specifically, we first design a Completion with External Guidance (CEG) module that leverages rich semantic information from external knowledge bases to expand the semantic boundary and accurately reconstruct the semantics of missing samples. Subsequently, we introduce a Consistency Learning with External Guidance (CLEG) module, which employs externally guided reconstructed features as anchors to align sample representations with label semantics, thereby effectively mitigating cross-modal bias. Finally, a Semantic-aware Contrastive Hashing (SCH) module is developed to refine the feature distribution by semantic similarity, pulling semantically related samples closer and pushing unrelated ones apart, thus achieving fine-grained discrimination among positive pairs. To the best of our knowledge, this is the first attempt to incorporate external knowledge into incomplete cross-modal hashing. Extensive experiments demonstrate that EGICH consistently and significantly outperforms 11 state-of-the-art methods under various modality-missing scenarios. The code is available at https://github.com/chenjiali27/EGICH.

The amount of multi-modal data available on the Internet is enormous. Cross-modal hash retrieval maps heterogeneous cross-modal data into a single Hamming space to offer fast and flexible retrieval services. However, existing cross-modal methods mainly rely on the feature-level similarity between multi-modal data and ignore the relationship between relative rankings and label-level fine-grained similarity of neighboring instances. To overcome these issues, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> eep <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ross-modal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</u> ashing based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> emantic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> onsistent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> anking (DCH-SCR) that comprehensively investigates the intra-modal semantic similarity relationship. Firstly, to the best of our knowledge, it is an early attempt to preserve semantic similarity for cross-modal hashing retrieval by combining label-level and feature-level information. Secondly, the inherent gap between modalities is narrowed by developing a ranking alignment loss function. Thirdly, the compact and efficient hash codes are optimized based on the common semantic space. Finally, we use the gradient to specify the optimization direction and introduce the Normalized Discounted Cumulative Gain (NDCG) to achieve varying optimization strengths for data pairs with different similarities. Extensive experiments on three real-world image-text retrieval datasets demonstrate the superiority of DCH-SCR over several state-of-the-art cross-modal retrieval methods.

Due to its cost-effective and high-efficiency retrieval advantages, deep hashing has attracted extensive attention in the field of cross-modal retrieval. However, despite significant progress in existing deep cross-modal hashing methods, several limitations persist: they struggle to establish consistent mapping relationships across different modalities, fail to effectively bridge the semantic gap between heterogeneous data, and consequently suffer from semantic information loss and incomplete semantic understanding during cross-modal learning. To address these challenges, this paper proposes a Feature Fusion-based Cross-modal Proxy Hashing (FFCPH) retrieval method. This approach integrates multi-modal semantic information through a feature fusion module to generate discriminative and robust fused features. Furthermore, a novel joint loss function, which comprises cross-modal proxy loss, cross-modal irrelevant loss, and cross-modal consistency loss, is designed to preserve inter-sample similarity ranking accuracy and mitigate the semantic gap across modalities. Experimental results on three widely used benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in retrieval performance.

Cross-modal retrieval faces new challenges due to the surge in multimedia content and internet technologies. Complex multi-label data, local semantic representation, and inter-modal feature imbalance are pitfalls for current deep cross-modal hashing techniques. We propose Feature Fusion Mamba Hashing via Decoupling (FFMHD) to address issues. A CLIP-based module aligns image-text features accurately. The decoupling enhancement module separates style and content, focusing retrieval on semantics. A hybrid state-space module combines convolutional local extraction with SS2D global perception for deep local semantics. Furthermore, a dynamic multi-semantic aligned hash loss refines sample relationships using quadratic spherical mutual information, modality-agnostic, and multi-label proxy losses. Experiments on MIRFLICKR-25 K, NUS-WIDE, and MS COCO show FFMHD outperforms chart-topper methods, achieving up to 6.1% mAP gain on I2T and 5.9% on T2I. Ablation studies validate the effectiveness of each module. Code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/KingKongBobbi/FFMHD</uri>.

Discrete online cross-modal hashing with consistency preservation