Fully supervised salient object detection (SOD) methods have made considerable progress in performance, yet these models rely heavily on expensive pixel-wise labels. Recently, to achieve a trade-off between labeling burden and performance, scribble-based SOD methods have attracted increasing attention. Previous scribble-based models directly implement the SOD task only based on SOD training data with limited information, it is extremely difficult for them to understand the image and further achieve a superior SOD task. In this paper, we propose a simple yet effective framework guided by general visual representations with rich contextual semantic knowledge for scribble-based SOD. These general visual representations are generated by self-supervised learning based on large-scale unlabeled datasets. Our framework consists of a task-related encoder, a general visual module, and an information integration module to efficiently combine the general visual representations with task-related features to perform the SOD task based on understanding the contextual connections of images. Meanwhile, we propose a novel global semantic affinity loss to guide the model to perceive the global structure of the salient objects. Experimental results on five public benchmark datasets demonstrate that our method, which only utilizes scribble annotations without introducing any extra label, outperforms the state-of-theart weakly supervised SOD methods. Specifically, it outperforms the previous best scribble-based method on all datasets with an average gain of 5.5% for max f-measure, 5.8% for mean f-measure, 24% for MAE, and 3.1% for E-measure. Moreover, our method achieves comparable or even superior performance to the state-of-the-art fully supervised models.

Recently, how to provide better POI recommendation performance by exploiting representation learning frame work is still one challenging task in LBSNs. Most existing methods either focus on modeling few limited information without considering other important information such as social influence and sentiment factor, or only rely on traditional shallow models to combine different factors, lacking effective integrating way to learn latent representations by fully utilizing multi-aspect interaction relations. To address these issues, we propose a novel sentiment-aware POI recommendation framework, dubbed as Senti2LSTM, which is capable of learning more comprehensive representations of users and POIs with fusion of multi-relations. Specifically, we first employ dual LSTMs to capture different sentimental embeddings for users and POIs respectively from emotional comments in LBSNs, and then we integrate them into the aggregation of propagation embeddings for users and POIs when learning their latent representations from user-POI bipartite graph and social link graph in LBSNs. Additionally, we also consider discriminating the importance of different social neighbors by leveraging social based attention mechanism, which makes social friends with common sentiments have more similar preferences.Finally,extensive experimental results conducted on two real-world datasets, e.g., Foursquare-NYC and Yelp2018, have demonstrated the effectiveness of our proposed Senti2LSTM in sentiment learning, and significantly outperforming the state-of-the-art POI recommendation methods

In this paper, a novel concept factorization (CF) method, called CF with adaptive neighbors (CFANs), is proposed. The idea of CFAN is to integrate an ANs regularization constraint into the CF decomposition. The goal of CFAN is to extract the representation space that maintains geometrical neighborhood structure of the data. Similar to the existing graph-regularized CF, CFAN builds a neighbor graph weights matrix. The key difference is that the CFAN performs dimensionality reduction and finds the neighbor graph weights matrix simultaneously. An efficient algorithm is also derived to solve the proposed problem. We apply the proposed method to the problem of document clustering on the 20 Newsgroups, Reuters-21578, and TDT2 document data sets. Our experiments demonstrate the effectiveness of the method.