Hashing method is widely used in multimedia retrieval systems because of its outstanding retrieval efficiency and low storage cost. Most existing unsupervised hashing methods learn binary hash codes through similarity structure preserving or contrastive learning of hash codes. However, these methods usually use the visual similarity of images to guide hash learning, which does not fully utilize the high-level semantic concept information contained in images, resulting in limited retrieval performance. To tackle this problem, we propose a novel deep unsupervised hashing method called Language Guidance Hashing (LGH). Specifically, LGH utilizes a language model to mine high-level semantic concept information in images and construct a language-based similarity structure, which is used to guide hash learning. By introducing features of textual modality, higher information gain can be brought. In addition, we also propose a language-guided contrastive learning method for learning high-quality binary hash codes. Extensive experimental results show that LGH significantly outperforms state-of-the-art unsupervised hashing methods on three benchmark image datasets.

Unsupervised hashing aims to learn a compact binary hash code to represent complex image content without label information. Existing deep unsupervised hashing methods typically first employ extracted image embeddings to construct semantic similarity structures and then map the images into compact hash codes while preserving the semantic similarity structure. However, the limited representation power of embeddings in Euclidean space and the inadequate exploration of the similarity structure in current methods often result in poorly discriminative hash codes. In this paper, we propose a novel method called Hyperbolic Multi-Structure Hashing (HMSH) to address these issues. Specifically, to increase the representation power of embeddings, we propose to map embeddings from Euclidean space to hyperbolic space and use the similarity structure constructed in hyperbolic space to guide hash learning. Meanwhile, to fully explore the structural information, we investigate four kinds of data structures, including local neighborhood structure, global clustering structure, inter/intra-class variation and variation under perturbation. Different data structures can complement each other, which is beneficial for hash learning. Extensive experimental results on three benchmark image datasets show that HMSH significantly outperforms state-of-the-art unsupervised hashing methods for image retrieval.

Cross-view geo-localization refers to matching ground images with geo-tagged satellite imagery. Existing methods are mainly two-stage, applying a polar transform to roughly eliminate the gap between these two domains, but this might introduce distortions and reduce the discriminativeness of features. In this work, we propose a transformer-based one-stage approach, which unifies gap elimination and feature extraction. The relative position among objects provides critical clues for this task and has strong spatial correspondences between the two views. Firstly, we form the relative position by selecting representative tokens from different regions. Then the relative positions of the two views predict each other and eliminate the gap through mutual learning. Finally, we introduce a novel consistency loss to enhance feature learning by mutual transfer of relational knowledge among samples. Extensive experiments demonstrate that our method achieves state-of-the-art results on both standard and fine-grained datasets.1

Hashing method has attracted more attention in recent years because of its low storage consumption and high retrieval performance. Most unsupervised hashing methods first construct local similarity structure in high-dimensional feature space, and then learn binary hash codes which maintain similarity structure information. However, this local structure based on pairwise distance will bring false guidance and misguide the hashing model. Besides, previous methods rarely consider the robustness of the hashing model, resulting in the unstable hash codes generated under perturbation. Toward these issues, we propose a novel Semantic Consistency Hashing (SCH). Specifically, to avoid misguidance caused by local similarity structure, SCH converts the similarity structure into the probability distribution and preserves semantic information from the perspective of global data distribution. In addition, to improve the robustness of hash codes, we introduce transformation consistency learning to maximize the similarity of hash codes under different transformations of the same image. Experiments on three popular datasets show that SCH outperforms the state-of-the-art methods.

Unsupervised hashing method aims to generate compact binary hash codes for images without label supervision. Existing unsupervised hashing methods usually learn binary hash codes by reconstructing input data or preserving similarity structures. However, these methods will either force the hash code to retain a large amount of redundant information or will learn a similarity structure with noise due to biased prior knowledge, resulting in poor retrieval performance. In this paper, we introduce a novel unsupervised hashing method called Masked Contrastive Hashing (MCH). Specifically, to maximally preserve meaningful semantic information into the binary hash code, MCH adopts an encoder-decoder structure and extracts the binary representation from the random masked image to reconstruct the original image. Furthermore, MCH maximizes the consistency of the enhanced views of the same image while minimizing the consistency of different images to establish the similarity relationship between images, which is helpful to generate hash codes that are more suitable for retrieval tasks. Extensive experiments show that the proposed MCH significantly outperforms existing state-of-the-art methods on several benchmark datasets.

Many existing unsupervised hashing methods attempt to preserve as much semantic information as possible by reconstructing the input data. However, this approach can result in the hash code preserving a lot of redundant information. Besides, previous works usually adopt local structures to guide hashing learning, which will mislead hashing model due to a large amount of noise existing in the local structure. In this paper, we propose a novel Deep Semantic Distillation Hashing (DSDH) to solve the above problems. Specifically, to ensure that the hashing model focuses on preserving more discriminative information rather than background noise, we use random masked images as input for feature extraction. We then apply empirical Maximum Mean Discrepancy to match the output feature distribution with that of the original image. Additionally, to avoid misleading, we propose to constrain the consistency of the similarity structures of the two spaces from the perspective of global distribution, thus transferring the knowledge of the feature space to Hamming space. Experiments conducted on three benchmarks show the superiority of DSDH.

Most of the existing unsupervised hashing methods usually construct semantic similarity structure to guide hashing learning. However, due to the lack of filtering of useless information, some wrong guiding information in the similarity structure may damage the retrieval performance. Besides, some works adopt the framework of contrastive learning to preserve the discriminative semantic information that is more important for the hashing task. But such a training strategy may incorrectly embed some semantically similar samples far away due to the absence of manual label supervision, thus producing sub-optimal hash codes. To solve the aforementioned problems, we propose a novel method named Deep Selective Semantic Mining Hashing (DSSMH). Specifically, with the prior knowledge obtained by clustering, we select semantically correct image pairs with high confidence to alleviate the guidance of wrong information and correct sampling bias in contrastive learning. Extensive experiments demonstrate that DSSMH outperforms existing state-of-the-art methods.

In consideration of the suitability for the application scenario, partial domain adaptation is more significant and more valuable than traditional domain adaptation. Most existing partial domain adaptation methods adopt weighting mechanism to avoid negative migration which is caused by outlier classes samples. However, these methods give the equal consideration of each category in the source domain and determine the classes weight by classifier or discriminator, and they do not consider the possible misprediction of the similar samples from classes which are difficult to distinguish in the source domain. This situation may cause the misalignment of the outlier source classes and target classes, and the wrong alignment of the discriminators. In this work, we propose a selective adversarial adaptation learning method via exclusive regularization for partial domain adaptation (ERPDA) to solve these problems. Specifically, we utilize the exclusive regularization to extend the distance between samples of different classes in source domain to learn an inter-class separable discriminant representation to avoid negative transfer. Meanwhile, the positive transfer is performed by Joint Maximum Mean Discrepancy (JMMD) based on selective adaptation adversarial learning via multi-discriminator. Extensive experiments show that ERPDA achieves state-of-the-art results on several partial domain adaptation benchmark datasets.