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With the rapid growth of multimedia data on the Internet, there has been a rapid rise in the demand for visual–textual cross-media retrieval between images and sentences. However, the heterogeneous property of visual and textual data brings huge challenges to measure the cross-media similarity for retrieval. Although existing methods have achieved great progress with the strong learning ability of the deep neural network, they rely heavily on the scale of training data with manual annotation, that is, either pairwise image–sentence annotation or category annotation as supervised information for visual–textual correlation learning, which are extremely labor and time consuming to collect. Without any pairwise or category annotation, it is highly challenging to construct a correlation between images and sentences due to their inconsistent distributions and representations. But people can naturally understand the correlation between visual and textual data in high-level semantic, and those images and sentences containing the same group of semantic concepts can be easily matched in human brain. Inspired by the above human cognitive process, this article proposes an unsupervised visual–textual correlation learning (UVCL) approach to construct correlations without any manual annotation. The contributions are summarized as follows: 1) unsupervised semantic-guided cross-media correlation mining is proposed to bridge the heterogeneous gap between visual and textual data. We measure the semantic matching degree between images and sentences, and generate descriptive sentences according to the concepts extracted from images to further augment the training data in an unsupervised manner. Therefore, the approach can exploit the semantic knowledge within both visual and textual data to reduce the gap between them for further correlation learning and 2) unsupervised visual–textual fine-grained semantic alignment is proposed to learn cross-media correlation by aligning the fine-grained visual local patches and textual keywords with fine-grained soft attention as well as semantic-guided hard attention, and the results can effectively highlight the fine-grained semantic information within both images and sentences to boost visual–textual alignment. Extensive experiments are conducted to perform visual–textual cross-media retrieval in unsupervised setting without any manual annotation on two widely used datasets, namely, Flickr-30K and MS-COCO, which verify the effectiveness of our proposed UVCL approach. [ABSTRACT FROM AUTHOR]

Multi-media data including image, video, text, audio, and 3D model, has been fast emerging on the Internet. Jointly correlating the data of various media types is a challenging task. With the considerable learning ability of deep network, existing works mainly construct multi-pathway network to learn cross-media correlation, where each pathway is for one media type. However, with number of media types increasing, existing methods face the problems of high repetition and complexity, leading to overfitting and poor generalization ability, which makes adverse effect on correlation learning. For addressing the above issues, we propose cross-media deep compression and regularization (CDCR) approach for quintuple-media joint correlation learning: 1) cross-media partial weight-sharing networks is proposed, where a part of parameters are commonly shared among multiple pathways, to exploit common characteristics across different media types for capturing intrinsic cross-media correlation; 2) we propose media-adaptive network pruning to drop connections between weakly-correlated neurons, which can emphasize media-specific characteristics adaptively; and 3) cross-media network regularization is proposed to utilize relationships among quintuple-media data, which can guarantee generalization ability and enhance intra-media and inter-media correlation. The experiments verify the effectiveness of our approach, which outperforms the state-of-the-art methods on two very challenging datasets, including a large-scale dataset PKU XMediaNet with more than 100 000 quintuple-media instances. [ABSTRACT FROM AUTHOR]

The rapidly developing information technology leads to a fast growth of visual and textual contents, and it comes with huge challenges to make correlation and perform cross-media retrieval between images and sentences. Existing methods mainly explore cross-media correlation from either global-level instances as the whole images and sentences, or local-level fine-grained patches as the discriminative image regions and key words, which ignore the complementary information from the relation between local-level fine-grained patches. Naturally, relation understanding is highly important for learning cross-media correlation. People focus on not only the alignment between discriminative image regions and key words, but also their relations lying in the visual and textual context. Therefore, in this paper, we propose Multi-level Adaptive Visual-textual Alignment (MAVA) approach with the following contributions. First, we propose cross-media multi-pathway fine-grained network to extract not only the local fine-grained patches as discriminative image regions and key words, but also visual relations between image regions as well as textual relations from the context of sentences, which contain complementary information to exploit fine-grained characteristics within different media types. Second, we propose visual-textual bi-attention mechanism to distinguish the fine-grained information with different saliency from both local and relation levels, which can provide more discriminative hints for correlation learning. Third, we propose cross-media multi-level adaptive alignment to explore global, local and relation alignments. An adaptive alignment strategy is further proposed to enhance the matched pairs of different media types, and discard those misalignments adaptively to learn more precise cross-media correlation. Extensive experiments are conducted to perform image-sentence matching on 2 widely-used cross-media datasets, namely Flickr-30K and MS-COCO, comparing with 10 state-of-the-art methods, which can fully verify the effectiveness of our proposed MAVA approach. [ABSTRACT FROM AUTHOR]

The heterogeneity gap leads to inconsistent distributions and representations between image and text, which rises a challenging task to measure their similarities and construct cross-media correlation between them. The existing works mainly model the cross-media correlation in a common subspace, which causes insufficient correlation modeling in such third-party subspace with intermediate unidirectional transformation. Inspired by the recent advances of neural machine translation, which aims to establish a corresponding relationship between two entirely different languages, we can naturally discover that it has striking common characteristic with cross-media correlation learning to consider image and text as bilingual pairs, where the image is treated as a special kind of language to provide visual description, so that bidirectional transformation can be conducted between image and text to effectively explore cross-media correlation in the feature space of each media type. Thus, we propose a reinforced cross-media bidirectional translation (RCBT) approach to model the correlation between visual and textual descriptions. First, cross-media bidirectional translation mechanism is proposed to conduct direct transformation between the bilingual pairs of visual and textual descriptions bidirectionally, where the cross-media correlation can be effectively captured in both feature spaces of image and text through bidirectional translation training. Second, cross-media context-aware network with residual attention is proposed to exploit the rich spatial and temporal context hints with cross-media convolutional recurrent neural network, which can lead to more precise correlation learning for promoting bidirectional translation process. Third, cross-media reinforcement learning is proposed to perform a two-agent communication game played as a round between image and text to boost the bidirectional translation process, and we further extract inter-media and intra-media reward signals to provide complementary clues for learning cross-media correlation. Extensive experiments are conducted on cross-media retrieval to verify the effectiveness of our proposed RCBT approach, compared with 11 state-of-the-art methods on three cross-media datasets. [ABSTRACT FROM AUTHOR]

Multimedia data with various modalities, such as image and text, are huge in quantity but have inconsistent distribution and representation. Many works have been done to break the boundary between image and text to measure their correlation. However, they focus on either the transformation to common subspace or the unidirectional generation from one to another individually, which cannot fully explore their interactions. It is noted that the bidirectional generation between image and text not only can provide complementary hints and mutually boost to learn cross-modal correlation but also cross-modal correlation learning can feed back to give comprehensive clues for promoting the cross-modal generation process. Therefore, we have the motivation that information transmission between image and text should be treated as a circular process, which aims to fully understand their latent correlation, and further realize cross-modal generation to produce both realistic images and text descriptions in a unified framework. In this paper, we propose the cross-modal circular correlation learning approach to perform both cross-modal correlation learning and generation simultaneously through an efficient circular learning training procedure. First, we propose the cross-modal circular learning model to perform an image-to-text caption and text-to-image synthesis circularly and learn common representation as a round-trip bridge, which can realize efficient interactions to fully exploit latent cross-modal correlations. Second, a unified bidirectional framework is proposed to conduct cross-modal mutual generation and is trained in an efficient circular process to enhance the generative ability of common representation, which can feed back circularly to further promote cross-modal correlation learning. In summary, we simultaneously perform cross-modal retrieval, image-to-text caption, and text-to-image synthesis in a unified framework with the circular learning process, which has high scalability and generality to realize universal cognition on the cross-modal data. We conduct extensive experiments to not only evaluate the correlation performance by cross-modal retrieval but also to show the generation effectiveness of both image caption and synthesis on the MS-COCO dataset. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval has become a highlighted research topic for retrieval across multimedia data such as image and text. A two-stage learning framework is widely adopted by most existing methods based on deep neural network (DNN): The first learning stage is to generate separate representation for each modality and the second learning stage is to get the cross-modal common representation. However the existing methods have three limitations: 1) In the first learning stage they only model intramodality correlation but ignore intermodality correlation with rich complementary context. 2) In the second learning stage they only adopt shallow networks with single-loss regularization but ignore the intrinsic relevance of intramodality and intermodality correlation. 3) Only original instances are considered while the complementary fine-grained clues provided by their patches are ignored. For addressing the above problems this paper proposes a cross-modal correlation learning (CCL) approach with multigrained fusion by hierarchical network and the contributions are as follows: 1) In the first learning stage CCL exploits multilevel association with joint optimization to preserve the complementary context from intramodality and intermodality correlation simultaneously. 2) In the second learning stage a multitask learning strategy is designed to adaptively balance the intramodality semantic category constraints and intermodality pairwise similarity constraints. 3) CCL adopts multigrained modeling which fuses the coarse-grained instances and fine-grained patches to make cross-modal correlation more precise. Comparing with 13 state-of-the-art methods on 6 widely-used cross-modal datasets the experimental results show our CCL approach achieves the best performance. [ABSTRACT FROM PUBLISHER]