The rapid dissemination of fake news and rumors through the Internet and social media platforms poses significant challenges and raises concerns in the public sphere. Automatic detection of fake news plays a crucial role in mitigating the spread of misinformation. While recent approaches have focused on leveraging neural networks to improve textual and visual representations in multi-modal fake news analysis, they often overlook the potential of incorporating knowledge information to verify facts within news articles. In this paper, we propose a knowledge enhanced vision and language model for multi-modal fake news detection. Our proposed model integrates information from large scale open knowledge graphs to augment its ability to discern the veracity of news content. Unlike previous methods that utilize separate models to extract textual and visual features, we synthesize a unified model capable of extracting both types of features simultaneously. To represent news articles, we introduce a graph structure where nodes encompass entities, relationships extracted from the textual content, and objects depicted in associated images. By utilizing the knowledge graph, we establish meaningful relationships between nodes within the news articles. Experimental evaluations on a real-world multi-modal dataset from Twitter demonstrate significant performance improvement by incorporating knowledge information.

Graph convolutional networks (GCNs) have achieved encouraging progress in modeling human body skeletons as spatial–temporal graphs. However, existing methods still suffer from two inherent drawbacks. Firstly, these models process the input data based on the physical structure of the human body, which leads to some latent correlations among joints being ignored. Furthermore, the key temporal relationships between nonadjacent frames are overlooked, preventing to fully learn the changes of the body joints along the temporal dimension. To address these issues, we propose an innovative spatial–temporal model by introducing a self-adaptive GCN (SAGCN) with global attention network, collectively termed SAGGAN. Specifically, the SAGCN module is proposed to construct two additional dynamic topological graphs to learn the common characteristics of all data and represent a unique pattern for each sample, respectively. Meanwhile, the global attention module (spatial attention (SA) and temporal attention (TA) modules) is designed to extract the global connections between different joints in a single frame and model temporal relationships between adjacent and nonadjacent frames in temporal sequences. In this manner, our network can capture richer features of actions for accurate action recognition and overcome the defect of the standard graph convolution. Extensive experiments on three benchmark datasets (NTU-60, NTU-120, and Kinetics) have demonstrated the superiority of our proposed method.

Session-based recommendation (SBR) aims to predict the next item at a certain time point based on anonymous user behavior sequences. Existing methods typically model session representation based on simple item transition information. However, since session-based data consists of limited users' short-term interactions, modeling session representation by capturing fixed item transition information from a single dimension suffers from data sparsity. In this paper, we propose a novel contrastive multi-level graph neural networks (CM-GNN) to better exploit complex and high-order item transition information. Specifically, CM-GNN applies local-level graph convolutional network (L-GCN) and global-level network (G-GCN) on the current session and all the sessions respectively, to effectively capture pairwise relations over all the sessions by aggregation strategy. Meanwhile, CM-GNN applies hyper-level graph convolutional network (H-GCN) to capture high-order information among all the item transitions. CM-GNN further introduces an attention-based fusion module to learn pairwise relation-based session representation by fusing the item representations generated by L-GCN and G-GCN. CM-GNN averages the item representations obtained by H-GCN to obtain high-order relation-based session representation. Moreover, to convert the high-order item transition information into the pairwise relation-based session representation, CM-GNN maximizes the mutual information between the representations derived from the fusion module and the average pool layer by contrastive learning paradigm. We conduct extensive experiments on several widely used benchmark datasets to validate the efficacy of the proposed method. The encouraging results demonstrate that our proposed method outperforms the state-of-the-art SBR techniques.

Unsupervised domain adaptation person re-identification (UDA person re-ID) aims at transferring the knowledge on the source domain with expensive manual annotation to the unlabeled target domain. Most of the recent papers leverage pseudo-labels for the target images to accomplish this task. However, the noise in the generated labels hinders the identification system from learning discriminative features. To address this problem, this paper proposes a deep mutual distillation (DMD) to generate reliable pseudo-labels for UDA person re-ID. The proposed DMD applies two parallel branches for feature extraction, and each branch serves as the teacher of the other to generate pseudo-labels for its training. This mutually reinforcing optimization framework enhances the reliability of pseudo-labels, improving the identification performance. In addition, this paper develops a bilateral graph representation (BGR) to describe the pedestrian images. BGR mimics the person re-identification of the human to aggregate the identity features according to the visual similarity and attribute consistency. Experimental results on Market-1501 and Duke demonstrate the effectiveness and generalization of the proposed method.

As an emerging computer vision task, crowd localization has received increasing attention due to its ability to produce more accurate spatially predictions. However, continuous scale variations in complex crowd scenes lead to tiny individuals at the edges, so that existing methods cannot achieve precise crowd localization. Aiming at alleviating the above problems, we propose a novel Dilated Convolution-based Feature Refinement Network (DFRNet) to enhance the representation learning capability. Specifically, the DFRNet is built with three branches that can capture the information of each individual in crowd scenes more precisely. More specifically, we introduce a Feature Perception Module to model long-range contextual information at different scales by adopting multiple dilated convolutions, thus providing sufficient feature information to perceive tiny individuals at the edge of images. Afterwards, a Feature Refinement Module is deployed at multiple stages of the three branches to facilitate the mutual refinement of feature information at different scales, thus further improving the expression capability of multi-scale contextual information. By incorporating the above modules, DFRNet can locate individuals in complex scenes more precisely. Extensive experiments on multiple datasets demonstrate that the proposed method has more advanced performance compared to existing methods and can be more accurately adapted to complex crowd scenes.