More and more users are getting used to posting images and text on social networks to share their emotions or opinions. Accordingly, multimodal sentiment analysis has become a research topic of increasing interest in recent years. Typically, there exist affective regions that evoke human sentiment in an image, which are usually manifested by corresponding words in peoples comments. Similarly, people also tend to portray the affective regions of an image when composing image descriptions. As a result, the relationship between image affective regions and the associated text is of great significance for multimodal sentiment analysis. However, most of the existing multimodal sentiment analysis approaches simply concatenate features from image and text, which could not fully explore the interaction between them, leading to suboptimal results. Motivated by this observation, we propose a new image-text interaction network (ITIN) to investigate the relationship between affective image regions and text for multimodal sentiment analysis. Specifically, we introduce a cross-modal alignment module to capture region-word correspondence, based on which multimodal features are fused through an adaptive cross-modal gating module. Moreover, considering the complementary role of context information on sentiment analysis, we integrate the individual-modal contextual feature representations for achieving more reliable prediction. Extensive experimental results and comparisons on public datasets demonstrate that the proposed model is superior to the state-of-the-art methods.

Image aesthetics assessment (IAA) has attracted growing interest in recent years but is still challenging due to its highly abstract nature. Nowadays, more and more people tend to comment images shared on the social networks, which can provide rich aesthetics-aware semantic information from different aspects. Therefore, user comments of an image can be exploited as supplementary information for enhancing aesthetic representation learning. Previous researches have demonstrated that aesthetic attributes make significant effect on image aesthetic quality and humans’ aesthetic perception. Typically, people are used to give comments on an image from the perspective of aesthetic attributes, based on which the aesthetic quality of images can be inferred. Motivated by this, this paper presents an Attribute-assisted Multimodal Memory Network (AMM-Net) for image aesthetics assessment, which utilizes aesthetic attributes to model the interactions between visual and textual modalities. Specifically, we design two memory networks to capture the attribute-aware information most related to the image and associated comments respectively. Further, with multiple memory hops, attribute semantics shared by the two modalities are refined and cross-modal interactions are enhanced progressively. Finally, more discriminative aesthetic representations can be obtained for IAA. The experimental results and comparisons on two public multimodal IAA datasets demonstrate the superiority of the proposed model over the state-of-the-art methods. The source code is available at https://github.com/zhutong0219/AMM-Net.

Nowadays, people are accustomed to posting images and associated text for expressing their emotions on social networks. Accordingly, multimodal sentiment analysis has drawn increasingly more attention. Most of the existing image-text multimodal sentiment analysis methods simply predict the sentiment polarity. However, the same sentiment polarity may correspond to quite different emotions, such as happiness vs. excitement and disgust vs. sadness. Therefore, sentiment polarity is ambiguous and may not convey the accurate emotions that people want to express. Psychological research has shown that objects and words are emotional stimuli and that semantic concepts can affect the role of stimuli. Inspired by this observation, this paper presents a new MUlti-Level SEmantic Reasoning network (MULSER) for fine-grained image-text multimodal emotion classification, which not only investigates the semantic relationship among objects and words respectively, but also explores the semantic relationship between regional objects and global concepts. For image modality, we first build graphs to extract objects and global representation, and employ a graph attention module to perform bilevel semantic reasoning. Then, a joint visual graph is built to learn the regional-global semantic relations. For text modality, we build a word graph and further apply graph attention to reinforce the interdependencies among words in a sentence. Finally, a cross-modal attention fusion module is proposed to fuse semantic-enhanced visual and textual features, based on which informative multimodal representations are obtained for fine-grained emotion classification. The experimental results on public datasets demonstrate the superiority of the proposed model over the state-of-the-art methods.

Different types of noise are inevitably introduced by devices during image acquisition and transmission processes. Therefore, image denoising remains a crucial challenge in computer vision. Deep learning, especially recent Transformer-based architectures, has demonstrated remarkable performance for image denoising tasks. However, due to its data-driven nature, deep learning can easily overfit the training data, leading to a lack of generalization ability. In order to address this issue, we present a novel Dynamic Mask-Enhanced Transformer (DMET) to improve the generalization capacity of denoising networks. Specifically, a texture-guided adaptive masking mechanism is introduced to simulate possible noise in practical applications. Then, we apply a masked hierarchical attention block to mitigate information loss and leverage global statistics, which combines shifted window multi-head self-attention with channel attention. Additionally, an attention mask is applied during training to reduce discrepancies between training and testing. Extensive experiments demonstrate that our approach achieves better generalization performance than state-of-the-art deep learning models and can be directly applied to real-world scenarios.