Deep clustering nowadays has proven to significantly surpass the classical clustering method, so it has been widely used in diverse applications. One current branch of deep clustering methods enhances the primary task through auxiliary tasks, among which the most prevalent is over-clustering, i.e., jointly training clustering with different numbers of clusters in a multi task manner. However, existing approaches typically treat these auxiliary tasks in isolation and neglect the inherent correlations among their cluster assignments. In this paper, we interpret the cluster assignment memberships of samples generated by all clustering tasks as correlated pseudo-labels. Motivated by this observation, we propose to explicitly exploit such correlation knowledge to improve clustering performance. To achieve this, we can formulate the collection of samples with pseudo-labels as a pseudo-multi-label learning problem, and solve it by employing any off-the-shelf multi-label learning methods which enable to capture correlations between pseudo-labels. Based on this idea, beyond the clustering tasks, we propose a correlation learning auxiliary task, namely Self-supervised Multi-Label Learning (SMLL); and we then specify a novel deep clustering method with SMLL, namely DCSL3. We conduct several experiments to examine the performance of DCSL3on benchmark datasets. Empirical results demonstrate the superiority of DCSL3over the existing deep clustering baseline methods.

The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Misinformation Detection (MD), aiming to detect online misinformation automatically, emerges as a rapidly growing research topic in the community. In this paper, we propose a novel plug-and-play augmentation method for the MD task, namely Misinformation Detection with Potential Commonsense Conflict (MD-PCC). We take inspiration from the prior studies indicating that fake articles are more likely to involve commonsense conflict. Accordingly, we construct commonsense expressions for articles, serving to express potential commonsense conflicts inferred by the difference between extracted commonsense triplet and golden ones inferred by the well-established commonsense reasoning tool COMET. These expressions are then specified for each article as augmentation. Any specific MD methods can be then trained on those commonsense-augmented articles. Besides, we also collect a novel commonsense-oriented dataset named CoMis, whose all fake articles are caused by commonsense conflict. We integrate MD-PCC with various existing MD backbones and compare them across both 4 public benchmark datasets and CoMis. Empirical results demonstrate that MD-PCC can consistently outperform the existing MD baselines.

Nowadays, misinformation articles, especially multimodal ones, are widely spread on social media platforms and cause serious negative effects. To control their propagation, Multimodal Misinformation Detection (MMD) becomes an active topic in the community to automatically identify misinformation. Previous MMD methods focus on supervising detectors by collecting offline data. However, in real-world scenarios, new events always continually emerge, making MMD models trained on offline data consistently outdated and ineffective. To address this issue, training MMD models under online data streams is an alternative, inducing an emerging task named continual MMD. Unfortunately, it is hindered by two major challenges. First, training on new data consistently decreases the detection performance on past data, named past knowledge forgetting. Second, the social environment constantly evolves over time, affecting the generalization on future data. To alleviate these challenges, we propose to remember past knowledge by isolating interference between event-specific parameters with a Dirichlet process-based mixture-of-expert structure, and anticipate future environmental distributions by learning a continuous-time dynamics model. Accordingly, we induce a new continual MMD method DAEDCMD. Extensive experiments demonstrate that DAEDCMD can consistently and significantly outperform the compared methods, including six MMD baselines and three continual learning methods.

Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to diverse aspects of our daily lives. To reduce the negative impact, timely identification of misinformation, namely Misinformation Detection (MD), has become an active research topic receiving widespread attention. As a complex phenomenon, the veracity of an article is influenced by various aspects. In this paper, we are inspired by the opposition of intents between misinformation and real information. Accordingly, we propose to reason the intent of articles and form the corresponding intent features to promote the veracity discrimination of article features. To achieve this, we build a hierarchy of a set of intents for both misinformation and real information by referring to the existing psychological theories, and we apply it to reason the intent of articles by progressively generating binary answers with an encoder-decoder structure. We form the corresponding intent features and integrate it with the token features to achieve more discriminative article features for MD. Upon these ideas, we suggest a novel MD method, namely Detecting Misinformation by Integrating Intent featuRes (DM-INTER). To evaluate the performance of DM-INTER, we conduct extensive experiments on benchmark MD datasets. The experimental results validate that DM-INTER can outperform the existing baseline MD methods.

The emerging eXtreme Multi-label Learning (XML) aims to induce multi-label predictive models from big datasets with extremely large numbers of instances, features, and especially labels. To meet the great efficiency challenge of XML, one flexible solution is the methodology of label tree, which, as its name suggests, is technically defined as a tree hierarchy of label subsets, partitioning the original large-scale XML problem into a number of small-scale sub-problems (i.e., denoted by leaf nodes) and then reducing the complexity to logarithmic time. Notably, the expected label trees should accurately find the right leaf nodes for future instances (i.e., effectiveness) and generate balanced leaf nodes (i.e., efficiency). To achieve this, we propose a novel generic method of label tree, namely Consistent, Balanced, and Overlapping Label Tree (CBOLT). To enhance the precision, we employ the weighted clustering to partition non-leaf nodes and allow overlapping label subsets, enabling to alleviate the inconsistent path and disjoint label subset issues. To improve the efficiency, we propose a new concept of a balanced problem scale and implement it with a balanced regularization for non-leaf nodes partition. We conduct extensive experiments on several benchmark XML datasets. Empirical results demonstrate that CBOLT is superior to the existing methods of label trees, and it can be applied to existing XML methods and achieve competitive performance with strong baselines.

Facial Expression Recognition (FER) is a basic and crucial computer vision task of classifying emotional expressions from human faces images into various emotion categories such as happy, sad, surprised, scared, angry, etc. Recently, facial expression recognition based on deep learning has made great progress. However, no matter the weight initialization technology or the attention mechanism, the face recognition method based on deep learning hard to capture those visually insignificant but semantically important features. To aid above question, in this paper we present a novel Facial Expression Recognition training strategy consisting of two components: Memo Affinity Loss (MAL) and Mask Attention Fine Tuning (MAFT). MAL is a variant of center loss, which uses memory bank strategy as well as discriminative center. MAL widens the distance between different clusters and narrows the distance within each cluster. Therefore, the features extracted by CNN were comprehensive and independent, which produced a more robust model. MAFT is a strategy that blindfolds attention parts temporarily and forces the model to learn from other important regions of the input image. It's not only an augmenting technique, but also a novel fine-tuning approach. As we know, we are the first to apply the mask strategy to the attention part and use this strategy to fine-tune the models. Finally, to implement our ideas, we constructed a new network named Architecture Attention ResNet based on ResNet-18. Our methods are conceptually and practically simple, but receives superior results on popular public facial expression recognition benchmarks with 88.75% on RAF-DB, 65.17% on AffectNet-7, 60.72% on AffectNet-8. The code will open source soon.

Image denoising is a well-studied yet still hot research topic in the image processing community. Recently, image denoising with deep neural networks has achieved superior performance, however they can not recover tiny details from noisy images. Motivated by this problem, we propose a Texture-Aware Neural Network named TANet, which is composed of main network part with attention mechanism, residual structure and Texture-Aware Modular. Proposed Texture-Aware Modular owns dual paths, denoised image from main denoising network and clean image are input different path respectively. From Texture-Aware Modular, we get two sets intermediate codes and calculate corresponding perceptual loss. This perceptual loss is designed to generate auxiliary super-vision for tiny detail recovery from mixed residual details and noise set. Extensive experimental results demonstrate that the proposed TANet is on a par with the state-of-the-art denoising methods.

Gaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space. This can take semantic information of words into account. However, the Euclidean similarity used in Gaussian topics is not an optimal semantic measure for word embeddings. Acknowledgedly, the cosine similarity better describes the semantic relatedness between word embeddings. To employ the cosine measure and capture complex topic structure, we use von Mises-Fisher (vMF) mixture models to represent topics, and then develop a novel mix-vMF topic model (MvTM). Using public pre-trained word embeddings, we evaluate MvTM on three real-world data sets. Experimental results show that our model can discover more coherent topics than the state-of-the-art baseline models, and achieve competitive classification performance.