Image matching can be transformed into the problem of feature point detection and matching of images. The current neural network methods have a weak detection effect on feature points and cannot extract enough sparse and uniform feature points. In order to improve the detection and description ability of feature points, this paper proposes a self‐supervised feature point detection and description network based on asymmetric convolution: ACPoint. Specifically, first, feature point pseudolabels are learned from an unlabeled dataset, and pseudolabels are used for supervised learning; then, the learned model is used to update pseudolabels. Through multiple iterations of model training and label updating, high‐quality labels and high‐accuracy models are obtained adaptively. The asymmetric convolution feature point (ACPoint) network adopts an asymmetric convolution module to simultaneously train three convolution branches to learn more feature information, which uses two one‐dimensional convolutions to enhance the backbone of square convolution from both horizontal and vertical directions and improve the representation of local features during inference. Based on the ACPoint network, a cross‐resolution image‐matching method is proposed. Experiments show that our proposed network model has higher localization accuracy and homography estimation ability on the HPatches dataset.

Cross-media hashing, which encodes data points from different modalities into a common Hamming space, has been successfully applied to solve large-scale multimedia retrieval issue due to storage efficiency and search effectiveness. Recently, matrix factorization based hashing methods have drawn considerable attention for their promising search accuracy. However, pioneer methods mainly focus on learning consensus hash codes for different modalities, but neglect the potential inconsistency among different modalities, \emph{e.g.,} the diversities of different modalities and noises, which may undermine the retrieval accuracy. To address this problem, we propose a novel unsupervised hashing model, namely, Discrete Robust Matrix Factorization Hashing (DRMFH), which simultaneously formulates the consistency and inconsistency across different modalities into a matrix factorization based model. Specifically, a homogenous space composed of a consistent Hamming space and an inconsistent diversity part, are generated by matrix factorization for each modality. Therefore, the consensus information across different modalities can be well captured in the learnt hash codes, leading to improved retrieval performance. Moreover, we design an effective optimization algorithm which is able to obtain an approximate discrete code matrix with linear time complexity. Comprehensive experimental results on three public multimedia retrieval datasets show that the proposed DRMFH outperforms several state-of-the-art methods.

The increasing popularity of social media facilitates the propagation of fake news, posing a major threat to the government and journalism, and thereby making how to detect fake news from social media an urgent requirement. In general, multimodal-based methods can achieve better performance because of the complementation among different modalities. However, the majority of them simply concatenate features from different modalities, failing to well preserve the mutual information in common features. To address this issue, a novel framework named semantic-enhanced multimodal fusion network is proposed for fake news detection, which can better capture mutual features among events and thus benefit the detection of fake news. This model consists of three subnetworks, namely multimodal fusion and event domain adaptation networks as well as the fake news detector. Specifically, the multimodal fusion network aims to extract deep features from texts and images and fuse them into a common semantic feature known as a snapshot. Then, the fake news detector can learn the representation of posts. Finally, the event domain adaptation network can single out and remove the peculiar features of each event, and keep shared features among events. The experimental results show that the proposed model outperforms some state-of-the-art approaches on two real-world multimedia data sets.