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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]

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas – fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community. [ABSTRACT FROM AUTHOR]