This paper combines the cultural tourism brand “Walking Henan ·Understanding China” with the online comments of Tiktoyin video. Through data mining technology, the online comment data is extracted, and the emotion classification of the comment data is carried out by the content analysis method. The comments are divided into positive comments, negative comments and neutral comments by the emotion analysis method. Moreover, content mining software ROST CM6 was used to construct semantic network maps of positive and negative comments respectively, and then the influencing factors of positive and negative comments were analyzed. Finally, countermeasures and suggestions were put forward to improve the brand influence of “Walking Henan $\cdot$Understanding China”.

As powerful tools deep neural networks have been successfully adopted for nuclei detection in histopathology images, whereas require the same probability distribution between training and testing data. However, domain shift among histopathology images widely exists in real-world applications and severely deteriorates the detection performance of deep neural networks. Despite encouraging results of existing domain adaptation methods, there remain challenges for cross-domain nuclei detection task. First, in view of the tiny size of nuclei, it is actually very difficult to obtain sufficient nuclei features, thus leading to a negative influence for feature alignment. Second, due to unavailable annotations in target domain, some extracted features contain background pixels and are thereby indiscriminative, which can largely confuse the alignment procedure. To address these challenges, in this paper, we propose an end-to-end graph-based nuclei feature alignment (GNFA) method for boosting cross-domain nuclei detection. Concretely, sufficient nuclei features are generated from nuclei graph convolutional network (NGCN) by aggregating information of adjacent nuclei upon construction of nuclei graph for successful alignment. In addition, importance learning module (ILM) is designed to further select discriminative nuclei features for mitigating negative influence of background pixels in target domain during alignment. By utilizing sufficient and discriminative node features generated from GNFA, our method can successfully perform feature alignment and effectively alleviate domain shift problem for nuclei detection. Extensive experiments of multiple adaptation scenarios reveal that our method achieves state-of-the-art performance in cross-domain nuclei detection compared with existing domain adaptation methods.

Ship detection in synthetic aperture radar (SAR) images is a basic and challenging task in marine monitoring. There has been made remarkable achievements in recent years. However, the existing detection methods still have the problem of ambiguous location information. It leads to the lack of model pertinence, and the poor discrimination of foreground and neighboring background. In this paper, we propose a ship detection method based on FSAF. In particular, we design a Gaussian Leading Block (GLB) capable to eliminate the interference of neighboring background information in the ground truth. Experiments on the HRSID dataset show that the proposed method achieves a 3.6% Average Precision (AP) improvement over the baseline at a low cost.

The emergence of Semi-Supervised Object Detection (SSOD) techniques has led to notable improvements in object detection capabilities by leveraging a restricted quantity of labeled data and a copious amount of unlabeled data. However, there are two challenging issues that need to be addressed in remote sensing images. Firstly, the complex background and large variation in target scales in remote sensing images can result in poor quality of pseudo-labels. Secondly, the long-tailed distribution problem, where some categories have a large number of instances while others have very few, is also common in remote sensing images. In this paper, we address SSOD in remote sensing images characterized by a long-tailed distribution. We propose an active learning strategy for selecting labeled data in the process of semi-supervised learning. The model training is decoupled into the training of backbone and detector. This idea contributes to favorable improvement in the regression branch and our method can achieve significant results on DOTA-v1.0 dataset.

In the field of remote sensing image object detection, the popular CNN-based methods need a large-scale and diverse dataset that is costly, and have limited generalization abili-ties for new categories. The few-shot object detection can be driven using only a few annotated samples. Existing few-shot detection methods are mainly designed for natural images, which ignore multi-scale objects and complex environments in remote sensing images. To tackle these challenges, we pro-pose a two-stage multi-scale method based on context mech-anism. Guided by the context-aware module, the multi-scale contextual information around the object is effectively extract and adaptively is combined into the ROI features to enhance the classification ability of the detector, which can reduce the classification confusion. Comparative experiments on public remote sensing image dataset RSOD show the effectiveness of our method.

Aircraft fine-grained recognition of remote sensing image is widely exploited in both military and civilian fields, which the similar physical structure and the changeable attitude between variable aircraft types makes this task challenge. Great progress has been made by proposal models based on convolutional neural network. However, previous works did not focus on local and multiple hierarchy features. In this paper, we propose a framework based on multiple hierarchy network and attention module for aircraft recognition. Compared to the existing methods, the extraction and enhancement of features proposed by us has been greatly improved. Remarkable results have been achieved on our dataset Aircraft-16 and the MTARSI dataset.

Recently, digital pathology image-based prognosis prediction has become a hot topic in healthcare research to make early decisions on therapy and improve the treatment quality of patients. Therefore, there has been a recent surge of interest in designing deep learning method solving the problem of prognosis prediction with digital pathology images. However, whole slide histopathological images (WSIs) based prognosis prediction is still a challenge due to the large size of pathological images, the heterogeneity of tumors and the high cost of region of interests (ROIs) labeling. In this study, we design a novel two-stage deep learning framework for prognosis prediction (TSDLPP) based on WSIs. Our proposed framework consists of two-stage paradigms: 1) training tissue decomposition network (TDNet) to divide WSIs into cancerous and non-cancerous regions, 2) integrating general prognosis-related densely connected CNN (GPR-DCCNN) and morphology-specific prognosis-related densely connected CNNs (MSPR-DCCNNs) to extract different level features of pathological images. In the end, we apply TSDLPP to the prognosis prediction of breast cancer using The Cancer Genome Atlas (TCGA) datasets. Experiment results demonstrate that TSDLPP obtains superior performance of prognosis prediction compared with the existing state-of-arts methods.

Multi-scale ship detection for remote sensing images is always a popular research field in civil and military application. In this paper, in order to solve the feature information single pathway flow in feature layer causes the lack of detailed information in the deep feature layer, we propose a bidirectional pathway feature pyramid networks (BP-FPN) method, which enables the deep feature layer to have strong semantic information as well as rich detailed information. At the same time, the Reverse Scale-transfer Layer down-sampling method is proposed to reduce the information loss of feature layer in the process of downsampling. It ensures that the feature layer maintain information during the down-sampling process. Experimental results on a dataset collected from Google Earth have quantitatively and qualitatively demonstrated the effectiveness of our approach

Accurate prow detection (i.e., ship heading prediction) is important in many applications that rely on optical remote sensing imagery, such as track forecasting and maritime navigation. In recent years, many advanced methods based on deep convolution neural networks (DCNNs) have succeeded in detecting multidirectional ships. However, these methods are not effective at determining the prow orientation, primarily due to three limitations: weak adaptability to geometric transformations of ship targets, confusing the semantic information between the prow and other parts of ships, and the boundary discontinuity problem. To address these problems, we propose an omnidirectional prow detection network (OPD-Net) based on feature enhancement and an improved regression model. OPD-Net consists of a feature refinement network (FRN), a prow attention network (PAN), and a complex plane coordinates regression model (CPCRM). First, the FRN balances the low-level location information and high-level semantic information from multiscale feature maps and then fits various geometric transformations regarding ship targets through deformable blocks. Next, the PAN, which is based on supervised learning, is used to enhance the ship prow feature as well as suppress background noise, which improves the accuracy of ship heading predictions. Finally, the CPCRM is designed to effectively solve the boundary discontinuity problem and correctly achieve prow detection in arbitrary orientations. Experiments on optical remote sensing image data sets demonstrate the robustness and superiority of our method for prow detection. Moreover, our approach is also competitive when used only for ship detection.

The persistent haze weather in North China has aroused extensive attention to environmental protection. Among all pollution resources, the anthropogenic emission by fossil fuel power plants plays an important role. To assist the environmental protection administration monitoring fossil fuel power plants, we propose an effective approach in this paper to learn an integrated model for chimney and condensing tower detection based on Faster R-CNN in high resolution remote sensing images. Our method can detect chimneys and condensing towers under different imaging condition efficiently and accurately. Experimental results on a self-collected dataset demonstrate the effectiveness of the proposed method.

Maneuvering targets tracking in cluttered environment is a challenging problem in computer vision because of the difficulty of distinguishing the target from the background. In this paper, we treat tracking as a binary classification problem and employ support vector machine to suppress the background. In order to enhance the robustness against illumination changes, we propose to combine color invariance with traditional RGB values to train the SVM. First, we use expectation maximization algorithm to extract the target from the environment; then, RGB and color invariance values are used to train SVM. In the incoming frames, pixels in regions of interest are classified by SVM and the confidence map is produced, which will afterward be used by traditional tracking approach to track the target, in this paper, we employ particle filter. Experimental results on challenging sequences validate the effectiveness of the proposed method in cluttered background target tracking.