Image captioning is one of the primary goals in computer vision which aims to automatically generate natural descriptions for images. Intuitively, human visual system can notice some stimulating regions at first glance, and then volitionally focus on interesting objects within the region. For example, to generate a free-form sentence about “boy-catch-baseball”, the visual region involving “boy” and “baseball” could be first attended and then guide the salient object discovery for the word-by-word generation. Till now, previous captioning works mainly rely on the object-wise modeling and ignore the rich regional patterns. To mitigate the drawback, this paper proposes the region-aware interaction learning method, which aims to explicitly capture the semantic correlations in the region and object dimensions for the word inference. First, given an image, we extract a set of regions which contain diverse objects and their relations. Second, we present the spatial-GCN interaction refining structure which can establish the connection between regions and objects to effectively capture contextual information. Third, we design the dual-attention interaction inference procedure, which enables attention to be calculated in region and object dimensions jointly for the word generation. Specifically, the guidance mechanism is proposed to selectively emphasize semantic inter-dependencies from region to object attentions. Extensive experiments on the MSCOCO dataset demonstrate the superiority of the proposed method. Additional ablation studies and visualization further validate its effectiveness.

Image-text retrieval is a fundamental and vital task in multi-media retrieval and has received growing attention since it connects heterogeneous data. Previous methods that perform well on image-text retrieval mainly focus on the interaction between image regions and text words. But these approaches lack joint exploration of characteristics and contexts of regions and words, which will cause semantic confusion of similar objects and loss of contextual understanding. To address these issues, a dual-level representation enhancement network (DREN) is proposed to strength the characteristic and contextual representations by innovative block-level and instance-level representation enhancement modules, respectively. The block-level module focuses on mining the potential relations between multiple blocks within each instance representation, while the instance-level module concentrates on learning the contextual relations between different instances. To facilitate the accurate matching of image-text pairs, we propose the graph correlation inference and weighted adaptive filtering to conduct the local and global matching between image-text pairs. Extensive experiments on two challenging datasets (i.e., Flickr30K and MSCOCO) verify the superiority of our method for image-text retrieval.

With the development of technologies such as multimedia technology and information technology, a great deal of video data is generated every day. However, storing and transmitting big video data requires a large quantity of storage space and network bandwidth because of its large scale. Therefore, the compression method of big video data has become a challenging research topic at present. Performance of existing content‐based video sequence compression method is difficult to be effectively improved. Therefore, in this paper, we present a fractal‐based parallel compression method without content for big video data. First of all, in order to reduce computational complexity, a video sequence is divided into several fragments according to the spatial and temporal similarity. Secondly, domain and range blocks are classified based on the color similarity feature to reduce computational complexity in each video fragment. Meanwhile, a fractal compression method is deployed in a SIMD parallel environment to reduce compression time and improve the compression ratio. Finally, experimental results show that the proposed method not only improves the quality of the recovered image but also improves the compression speed by compared with existing compression algorithms.

Image captioning aims at understanding various semantic concepts (e.g., objects and relationships) from an image and integrating them in a sentence-level description. Hence, it is necessary to learn the interaction among these concepts. If we define the context of the interaction to be involved in the subject-predicate-object triplet, most current methods only focus on the single triplet for the first-order interaction to generate sentences. Intuitively, we humans are able to perceive the high-order interaction among concepts from two or more triplets to describe an image. For example, when we see the triplets man-cutting-sandwich and man-with-knife , it is natural to integrate and predict the sentence man cutting sandwich with knife . This depends on the high-order interaction between cutting and knife in different triplets. Therefore, exploiting high-order interaction is expected to benefit image captioning and focus on reasoning. In this paper, we introduce the novel high-order interaction learning method over detected objects and relationships for image captioning under the umbrella of the encoder-decoder framework. We first extract a set of object and relationship features in an image. During the encoding stage, the interactive refining network is proposed to learn high-order representations by modeling intra- and inter-object feature interaction in the self-attention fashion. During the decoding stage, the interactive fusion network is proposed to integrate object and relationship information by strengthening their high-order interaction based on language context for sentence generation. In this way, we learn the object-relationship dependencies in different stages, which can provide abundant cues for both visual understanding and caption generation. Extensive experiments show that the proposed method can achieve competitive performances against the state-of-the-art methods on MSCOCO dataset. Additional ablation studies further validate its effectiveness.

As an intersection of artificial intelligence and human communication analysis, Emotion Recognition in Conversation (ERC) has attracted much research attention in recent years. Existing studies, however, are limited in adequately exploiting latent relations among the constituent utterances. In this paper, we address this issue by proposing a novel approach named Latent Relation-Aware Graph Convolutional Network (LR-GCN), where both speaker dependency of the interlocutors is leveraged and latent correlations among the utterances are captured for ERC. Specifically, we first establish a graph model to incorporate the context information and speaker dependency of the conversation. Afterward, the multi-head attention mechanism is introduced to explore the latent correlations among the utterances and generate a set of all-linked graphs. Here, aiming to simultaneously exploit the original modeled speaker dependency and the explored correlation information, we introduce a dense connection layer to capture more structural information of the generated graphs. Through a multi-branch graph network, we achieve a unified representation of each utterance for final prediction. Detailed evaluations on two benchmark datasets demonstrate LR-GCN outperforms the state-of-the-art approaches.

Image and text matching plays a crucial role in bridging the cross-modal gap between vision and language, and has achieved great progress due to the deep learning. However, the existing methods still suffer from the long-tail problem, where only a small proportion contains highly frequent semantics and a long tail proportion is constructed by rare semantics. In this paper, we propose a novel Dual-path Rare Content Enhancement Network (DRCE) to tackle the long-tail issue. Specifically, the Cross-modal Representation Enhancement (CRE) and Cross-modal Association Enhancement (CAE) are proposed to construct dual-path structure to enhance rare content representation and association with the benefit of cross-modal prior knowledge. This structure can effectively exploit the complementary cross-modal relation from different aspects and fuse these information in an adaptively manner by the proposed Adaptive Fusion Strategy (AFS). Moreover, we also propose an alternative re-ranking strategy (ARR) to explore the reciprocal contextual information to refine image-text matching results, which can further suppress the negative effect of long-tail effect. Extensive experiments on two large-scale datasets show the significant improvements and validate the superiority of our method.