To reduce the harmful effects of rain for visual tasks， rain removal algorithms are commonly utilized on single frame images or video streams to remove rain. However， since rain falls extremely fast， frame-based cameras cannot capture the temporal continuity of rain， and the fixed exposure time and motion blur further reduce the sharpness of the rain on images， as a result， the traditional image rain removal algorithms cannot detect rain coverage areas accurately. In order to explore the new idea of image rain removal， a rain event generation model was constructed and a rain detection algorithm for event camera based on spatial-temporal relevance was proposed by using the characteristics of event camera： extremely high sampling rate and no motion blur. In this algorithm， the probability of each event generated by rain movement was calculated by analyzing the spatial-temporal relationship between each event recorded by the event camera and adjacent events， so as to achieve rain detection. Experimental results on three rainfall scenes show that when the camera is static， the proposed algorithm can reach more than 95% rain detection true positive rate， and the false positive rate less than 5%， and when the camera moves， the proposed algorithm can still reach more than 95% true positive rate and no more than 20% false positive rate. The above shows that the rain can be detected effectively by the proposed algorithm.
Node feature representation was learned by Graph Convolutional Network （GCN） by deep graph matching models in the stage of node feature extraction. However， GCN was limited by the learning ability for node feature representation， affecting the distinguishability of node features， which causes poor measurement of node similarity， and leads to the loss of model matching accuracy. To solve the problem， a deep graph matching model based on self-attention network was proposed. In the stage of node feature extraction， a new self-attention network was used to learn node features. The principle of the network is improving the feature description of nodes by utilizing spatial encoder to learn the spatial structures of nodes， and using self-attention mechanism to learn the relations among all the nodes. In addition， in order to reduce the loss of accuracy caused by relaxed graph matching problem， the graph matching problem was modelled to an integer linear programming problem. At the same time， structural matching constraints were added to graph matching problem on the basis of node matching， and an efficient combinatorial optimization solver was introduced to calculate the local optimal solution of graph matching problem. Experimental results show that on PASCAL VOC dataset， compared with Permutation loss and Cross-graph Affinity based Graph Matching （PCA-GM）， the proposed model has the average matching precision on 20 classes of images increased by 14.8 percentage points， on Willow Object dataset， the proposed model has the average matching precision on 5 classes of images improved by 7.3 percentage points， and achieves the best results on object matching tasks such as bicycles and plants.
Existing works on influence maximization mainly focus on unsigned network and neglect the hostile relationship between the individuals in the network. Aiming at the positive influence maximization problem in signed network， based on Polarity-related Independent Cascade （IC-P） model， a Reverse Influence Sampling in Signed network （RIS-S） algorithm was proposed to maximize positive influence. Firstly， in order to apply to the signed network， the polarity relationships of nodes in the stage of generating reverse reachable sets were considered. Secondly， to improve the effectiveness of reverse reachable sets， the traversal depth of sampling was limited. Finally， the positive influence ranges and running times of RIS-S， Influence Maximization via Martingales （IMM）， Positive Out-Degree （POD） and Effective Degree algorithm were compared on three real signed network data sets to verify the effectiveness of the proposed algorithm. Experimental results show that RIS-S algorithm can obtain wider positive influence range by selecting more accurate seeds， and the proposed algorithm has the running time less than the same type algorithm IMM.It can be thought that RIS-S algorithm can solve the problem of positive influence maximization in signed network.
Camouflaged Object Detection （COD） aims to detect objects hidden in complex environments. The existing COD algorithms ignore the influence of feature expression and fusion methods on detection performance when combining multi-level features. Therefore， a COD algorithm based on progressive feature enhancement aggregation was proposed. Firstly， multi-level features were extracted through the backbone network. Then， in order to improve the expression ability of features， an enhancement network composed of Feature Enhancement Module （FEM） was used to enhance the multi-level features. Finally， Adjacency Aggregation Module （AAM） was designed in the aggregation network to achieve information fusion between adjacent features to highlight the features of the camouflaged object area， and a new Progressive Aggregation Strategy （PAS） was proposed to aggregate adjacent features in a progressive way to achieve effective multi-level feature fusion while suppressing noise. Experimental results on 3 public datasets show that the proposed algorithm achieves the best performance on 4 objective evaluation indexes compared with 12 state-of-the-art algorithms， especially on COD10K dataset， the weighted F-measure and the Mean Absolute Error （MAE） of the proposed algorithm reach 0.809 and 0.037 respectively. It can be seen that the proposed algorithm achieves better performance on COD tasks.
