Self-supervised multiplex graph representation learning (SMGRL) has attracted increasing interest, but previous SMGRL methods still suffer from the following issues: (i) they focus on the common information only (but ignore the private information in graph structures) to lose some essential characteristics related to downstream tasks, and (ii) they ignore the redundant information in node representations of each graph. To solve these issues, this paper proposes a new SMGRL method by jointly mining the common information and the private information in the multiplex graph while minimizing the redundant information within node representations. Specifically, the proposed method investigates the decorrelation losses to extract the common information and minimize the redundant information, while investigating the reconstruction losses to maintain the private information. Comprehensive experimental results verify the superiority of the proposed method, on four public benchmark datasets.

Recently, with the superior capacity for analyzing the multiplex graph data, self-supervised multiplex graph representation learning (SMGRL) has received much interest. However, existing SMGRL methods are still limited by the following issues: (i) they generally ignore the noisy information within each graph and the common information among different graphs, thus weakening the effectiveness of SMGRL, and (ii) they conduct negative sample encoding and complex pretext tasks for contrastive learning, thus weakening the efficiency of SMGRL. To solve these issues, in this work, we propose a new framework to conduct effective and efficient SMGRL. Specifically, the proposed method investigates the intra-graph and inter-graph decorrelation losses, respectively, for reducing the impact of noisy information within each graph and capturing the common information among different graphs, to achieve the effectiveness. Moreover, the proposed method does not need negative samples for the SMGRL and designs a simple pretext task, to achieve the efficiency. We further theoretically justify that our method achieves the maximal mutual information instead of directly conducting contrastive learning and theoretically justify that our method actually minimizes the multiplex graph information bottleneck, which guarantees the effectiveness. In addition, an extension for semi-supervised scenarios is proposed to fit the case that a few labels are provided in reality. Extensive experimental results verify the effectiveness and efficiency of the proposed method with respect to various downstream tasks.

Self-supervised multiplex graph representation learning (SMGRL) aims to capture the information from the multiplex graph, and generates discriminative embedding without labels. However, previous SMGRL methods still suffer from the issues of efficiency and effectiveness due to the processes, e.g., data augmentation, negative sample encoding, complex pretext tasks, etc. In this paper, we propose a simple method to achieve efficient and effective SMGRL. Specifically, the proposed method removes the processes (i.e., data augmentation and negative sample encoding) for the SMGRL and designs a simple pretext task, for achieving the efficiency. Moreover, the proposed method also designs an intra-graph decorrelation loss and an inter-graph decorrelation loss, respectively, to capture the common information within individual graphs and the common information across graphs, for achieving the effectiveness. Extensive experimental results verify the efficiency and effectiveness of our method, compared to 11 comparison methods on 4 public benchmark datasets, on the node classification task.

Recently, deep learning techniques have substantially boosted the performance of salient object detection in still images. However, the salient object detection in videos by using traditional handcrafted features or deep learning features is not fully investigated, probably due to the lack of sufficient manually labeled video data for saliency modeling, especially for the data-driven deep learning. This paper proposes a novel weakly supervised approach to the salient object detection in a video, which can learn a robust saliency prediction model by using very limited manually labeled data and a large amount of weakly labeled data that could be easily generated in a supervised approach. Furthermore, we propose a spatiotemporal cascade neural network architecture for saliency modeling, in which two fully convolutional networks are cascaded to evaluate the visual saliency from both spatial and temporal cues to lead the optimal video saliency prediction. The proposed approach is extensively evaluated on the widely used challenging data sets, and the experiments demonstrate that our proposed approach substantially outperforms the state-of-the-art salient object detection models.

This paper focuses on salient object detection in video by multiple time-scale analysis, which exploits the temporally consistent information under three different scales. In the first time-scale, we define an effective measure called motion contrast from both low-level cues and the optical flow fields. In the second time-scale, we propose a novel approach to repair the inaccurate motion contrast due to the mistake of optical flow. In the third time-scale, considering the low-contrast objects that stop moving for a certain amount of time and cannot remain prominent, we present a robust motion detection method based on point-tracking and trajectories clustering. Finally, the outcomes from the three time-scales jointly formulate the saliency detection by Bayesian inference. The proposed model is evaluated on the widely-used DAVIS and FBMS benchmark. Experiments demonstrate that our proposed model substantially outperforms the state-of-the-art saliency detection models.

This paper presents a novel model for video salient object detection called spatiotemporal constrained optimization model (SCOM), which exploits spatial and temporal cues, as well as a local constraint, to achieve a global saliency optimization. For a robust motion estimation of salient objects, we propose a novel approach to modeling the motion cues from optical flow field, the saliency map of the prior video frame and the motion history of change detection, which is able to distinguish the moving salient objects from diverse changing background regions. Furthermore, an effective objectness measure is proposed with intuitive geometrical interpretation to extract some reliable object and background regions, which provided as the basis to define the foreground potential, background potential, and the constraint to support saliency propagation. These potentials and the constraint are formulated into the proposed SCOM framework to generate an optimal saliency map for each frame in a video. The proposed model is extensively evaluated on the widely used challenging benchmark data sets. Experiments demonstrate that our proposed SCOM substantially outperforms the state-of-the-art saliency models.