Fake news detection attracts many researchers’ attention due to the negative impacts on the society. Most existing fake news detection approaches mainly focus on semantic analysis of news’ contents. However, the detection performance will dramatically decrease when the content of news is short. In this paper, we propose a novel fake news detection multi-task learning (FDML) model based on the following observations: 1) some certain topics have higher percentages of fake news; and 2) some certain news authors have higher intentions to publish fake news. FDML model investigates the impact of topic labels for the fake news and introduce contextual information of news at the same time to boost the detection performance on the short fake news. Specifically, the FDML model consists of representation learning and multi-task learning parts to train the fake news detection task and the news topic classification task, simultaneously. As far as we know, this is the first fake news detection work that integrates the above two tasks. The experiment results show that the FDML model outperforms state-of-the-art methods on real-world fake news dataset.

Unsupervised person re-identification is a challenging and promising task in computer vision. Nowadays unsupervised person re-identification methods have achieved great progress by training with pseudo labels. However, how to purify feature and label noise is less explicitly studied in the unsupervised manner. To purify the feature, we take into account two types of additional features from different local views to enrich the feature representation. The proposed multi-view features are carefully integrated into our cluster contrast learning to leverage more discriminative cues that the global feature easily ignored and biased. To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme. Specifically, we first train a teacher model from noisy pseudo labels, and then use the teacher model to guide the learning of our student model. In our setting, the student model could converge fast with the supervision of the teacher model thus reduce the interference of noisy labels as the teacher model greatly suffered. After carefully handling the noise and bias in the feature learning, our purification modules are proven to be very effective for unsupervised person re-identification. Extensive experiments on two popular person re-identification datasets demonstrate the superiority of our method. Especially, our approach achieves a state-of-the-art accuracy 85.8% @mAP and 94.5% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under the fully unsupervised setting. The code will be released.

Siamese trackers contain two core stages, i.e., learning the features of both target and search inputs at first and then calculating response maps via the cross-correlation operation, which can also be used for regression and classification to construct typical one-shot detection tracking framework. Although they have drawn continuous interest from the visual tracking community due to the proper trade-off between accuracy and speed, both stages are easily sensitive to the distracters in search branch, thereby inducing unreliable response positions. To fill this gap, we advance Siamese trackers with two novel non-local blocks named Nocal-Siam, which leverages the long-range dependency property of the non-local attention in a supervised fashion from two aspects. First, a target-aware non-local block (T-Nocal) is proposed for learning the target-guided feature weights, which serve to refine visual features of both target and search branches, and thus effectively suppress noisy distracters. This block reinforces the interplay between both target and search branches in the first stage. Second, we further develop a location-aware non-local block (L-Nocal) to associate multiple response maps, which prevents them inducing diverse candidate target positions in the future coming frame. Experiments on five popular benchmarks show that Nocal-Siam performs favorably against well-behaved counterparts both in quantity and quality.

Semi-supervised learning (SSL) that utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples is a powerful learning paradigm with widely real-world applications such as information retrieval and document clustering. Label propagation (LP) is a popular SSL method which propagates labels through the dataset along high density areas defined by unlabeled examples, but it is fragile to bridge examples. Semi-supervised K-Means uses labeled examples to initialize clustering centers to separate different examples, however, semi-supervised K-Means fails in the situation of imbalanced issues, that is, the example size of each class varies significantly. This paper proposes a novel label propagated nonnegative matrix factorization method (LPNMF) to handle clean labeled but biased data and its extension LPNMF-E to handle noisy labeled data based on the framework of NMF. LPNMF decomposes the whole dataset into the product of a basis matrix and a coefficient matrix. To propagate labels to unlabeled examples, LPNMF regards the class indicators of labeled examples as their coefficients and iteratively updates both basis matrix and coefficients of unlabeled examples. LPNMF absorbs the merits from both semi-supervised K-Means and label propagation to handle their respective shortages. Specifically, on the one hand, LPNMF learns representative clustering centers based on the distribution of the dataset, similar to semi-supervised K-means, and thus is robust to the bridge examples. On the other hand, LPNMF pushes labels according to the affinity between examples, similar to label propagation, and thus relieves the biased problem. Moreover, we introduce a LPNMF extension to handle the noisy label case. LPNMF-E relaxes the constraint of labeled examples. Since the label of each labeled example also obtains label information from the global distribution of the whole dataset and local manifold of its neighbors, LPNMF-E outputs reliable class indicators even if a portion of examples are incorrectly labeled. Theoretical analyses for the generalization ability of our proposed models are also provided. Experimental results on both clean and noisy labeled datasets confirm the effectiveness of LPNMF and LPNMF-E compared with both LP and the representative semi-supervised K-Means algorithms.

Stock trend prediction refers to predicting future price trend of stocks for seeking profit maximum of stock investment. Although it has aroused broad attention in stock markets, it is still a tough task not only because the stock markets are complex and easily volatile but also because real short-term stock data is so limited that existing stock prediction models could be far from perfect, especially for deep neural networks. As a kind of time-series data, the underlying patterns of stock data are easily influenced by any tiny noises. Thus, how to augment limited stock price data is an open problem in stock trend prediction, since most data augmentation schemes adopted in image processing cannot be brutally used here. To this end, we devise a simple yet effective time-sensitive data augmentation method for stock trend prediction. To be specific, we augment data by corrupting high-frequency patterns of original stock price data as well as preserving low-frequency ones in the frame of wavelet transformation. The proposed method is motivated by the fact that low-frequency patterns without noisy corruptions do not hurt the true patterns of stock price data. Besides, a transformation technique is proposed to recognize the importance of the patterns at varied time points, that is, the information is time-sensitive. A series of experiments carried out on a real stock price dataset including 50 corporation stocks verify the efficacy of our data augmentation method.