Cross-modal hashing has sparked much attention in large-scale information retrieval for its storage and query efficiency. Despite the great success achieved by supervised approaches, existing unsupervised hashing methods still suffer from the lack of reliable learning guidance and cross-modal discrepancy. In this paper, we propose Aggregation-based Graph Convolutional Hashing (AGCH) to tackle these obstacles. First, considering that a single similarity metric can hardly represent data relationships comprehensively, we develop an efficient aggregation strategy that utilises multiple metrics to construct a more precise affinity matrix for learning. Specifically, we apply various similarity measures to exploit the structural information of multiple modalities from different perspectives and then aggregate the obtained information to produce a joint similarity matrix. Furthermore, a novel deep model is designed to learn unified binary codes across different modalities, where the key components include modality-specific encoders, Graph Convolutional Networks (GCNs) and a fusion module. The modality-specific encoders are tasked to learn feature embeddings for each individual modality. On this basis, we leverage GCNs to further excavate the semantic structure of data, along with a fusion module to correlate different modalities. Extensive experiments on three real-world datasets demonstrate that the proposed method significantly outperforms the state-of-the-art competitors.

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks. Given a sequence of interacted items, existing DNN-based sequential recommenders commonly embed each item into a unique vector to support subsequent computations of the user interest. However, due to the potentially large number of items, the over-parameterised item embedding matrix of a sequential recommender has become a memory bottleneck for efficient deployment in resource-constrained environments, e.g., smartphones and other edge devices. Furthermore, we observe that the widely-used multi-head self-attention, though being effective in modelling sequential dependencies among items, heavily relies on redundant attention units to fully capture both global and local item-item transition patterns within a sequence.

Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.

Deep learning techniques have ushered in significant progress in large-scale multi-modal retrieval. Nevertheless, the advanced techniques may be used nefariously to conduct a search that violates the privacy of individuals. In this paper, we propose a novel PrIvacy Protection method (PIP) against malicious multi-modal retrieval models, which proactively transfers original data into adversarial data with quasi-imperceptible perturbations before releasing them. Consequently, unauthorized malicious parties are not able to use deployed deep models to find out desired sensitive information with them. In addition to privacy preserving, PIP synchronously learns an effective multi-modal retrieval model to facilitate authorized uses, endowed with strong resilience to the perturbations. To the best of our knowledge, it is a very first attempt to consider privacy issues in multi-modal retrieval, and encapsulate both privacy protection against unauthorized retrieval and robust multi-modal learning for authorized uses into a unified framework. This work is conducted in the challenging no-box and unsupervised settings, where neither target malicious models nor supervised information is known. The optimization objective of our versatile PIP is achieved through a two-player game between different components with both the intra- and inter-modality graph alignments and the domain distribution alignment considered. Besides, a high-level similarity matrix is developed to obtain reliable guidance for learning. Empirically, we apply the proposed PIP to hashing based multi-modal retrieval scenarios and prove its effectiveness on a range of benchmarks and tasks.

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression. Nevertheless, existing approaches could hardly guarantee a satisfactory performance with the extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss and the shrink of the discrete solution space. In this article, we propose a novelCollaborative Learningstrategy that is tailored for generating high-quality low-bit hash codes. The core idea is to jointly distill bit-specific and informative representations for a group of pre-defined code lengths. The learning of short hash codes among the group can benefit from the manifold shared with other long codes, where multiple views from different hash codes provide the supplementary guidance and regularization, making the convergence faster and more stable. To achieve that, an asymmetric hashing framework with two variants of multi-head embedding structures is derived, termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of training and querying. Extensive experiments on three benchmark datasets have been conducted to verify the superiority of the proposed MAH, and have shown that the 8-bit hash codes generated by MAH achieve 94.3 percent of the MAP11.Mean Average Precision (MAP)score on the CIFAR-10 dataset, which significantly surpasses the performance of the 48-bit codes by the state-of-the-arts in image retrieval tasks.

Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in largescale multimedia retrieval applications. Particularly, supervised hashing has recently received considerable research attention by leveraging the label information to preserve the pairwise similarities of data points in the Hamming space. However, there still remain two crucial bottlenecks: 1) the learning process of the full pairwise similarity preservation is computationally unaffordable and unscalable to deal with big data; 2) the available category information of data are not well-explored to learn discriminative hash functions. To overcome these challenges, we propose a unified Semantic-Aware DIscrete Hashing (SADIH) framework, which aims to directly embed the transformed semantic information into the asymmetric similarity approximation and discriminative hashing function learning. Specifically, a semantic-aware latent embedding is introduced to asymmetrically preserve the full pairwise similarities while skillfully handle the cumbersome n×n pairwise similarity matrix. Meanwhile, a semantic-aware autoencoder is developed to jointly preserve the data structures in the discriminative latent semantic space and perform data reconstruction. Moreover, an efficient alternating optimization algorithm is proposed to solve the resulting discrete optimization problem. Extensive experimental results on multiple large-scale datasets demonstrate that our SADIH can clearly outperform the state-of-the-art baselines with the additional benefit of lower computational costs.

With the widespread availability of image captioning at a sentence level, how to automatically generate image paragraphs is yet well explored. Describing an image by a full paragraph involves organising sentences orderly, coherently and diversely, inevitably leading higher complexity than by a single sentence. Existing image paragraph captioning methods give a series of sentences to represent the objects and regions of interests, where the descriptions are essentially generated by feeding the image fragments containing objects and regions into conventional image single-sentence captioning models. This strategy is difficult to generate the descriptions that guarantee the stereoscopic hierarchy and non-overlapping objects. In this paper, we propose a Depth-aware Attention Model (\textitDAM ) to generate paragraph captions for images. The depths of image areas are firstly estimated in order to discriminate objects in a range of spatial locations, which can further guide the linguistic decoder to reveal spatial relationships among objects. This model completes the paragraph in a logical and coherent manner. By incorporating the attention mechanism, the learned model swiftly shifts the sentence focus during paragraph generation, whilst avoiding verbose descriptions on a same object. Extensive quantitative experiments and the user study have been conducted on the Visual Genome dataset, which demonstrate the effectiveness and the interpretability of the proposed model.