Synthetic aperture radar (SAR) ship detection plays an important role in the field of maritime security. However, certain unique imaging properties make it challenging to extract the shape features of ships, such as speckle noise and strong scattering interference from irrelevant objects. These factors result in inaccurate ship localization and obvious false alarms under complex large-scale inshore scenes. To address this issue, we propose the scattering region topology network (SRT-Net), which can dynamically capture the comprehensive global context and enhance the ship saliency. This is achieved through two key modules, namely the scattering region topological structure pyramid (SRTP) and the ship saliency enhancement (SSE) module. The former provides richer semantic information to distinguish the object from the background, while the latter offers an extra pixel-level classification task to guide accurate bounding box regression. Thanks to the guidance of richer information, the proposed method can achieve fewer false alarms and enhance location accuracy. Additionally, we introduce a scale feature adaptive (SFA) loss to balance the attention to ships with various scales, which improves the robustness of multiscale ship detection. The proposed method achieves state-of-the-art performance under complex inshore scenes, and its effectiveness is verified by experiments on a large-scale SAR ship detection dataset (LSSDD) and a public SAR ship detection dataset (SSDD+).

The grouping process of corner-based detectors still faces two challenges: 1) Hard-grouping. If one of the paired corners is wrongly estimated, the grouping goes wrong. 2) Complex pipeline. Vanilla methods regard corner grouping as an additional stage, complicating the post-processing. To eliminate these issues, we propose a novel grouping algorithm, termed as Soft-Grouping Non-Maximum Suppression (SG-NMS), which merges grouping with NMS into a whole to simplify the pipeline and lift the efficiency. SG-NMS is flexible to match the varied number of detected corners. Accordingly, we propose a multi-corners context enhanced module, Corner Visual Reasoning (CVR), as a grouping helper. Equipped with the proposed SG-NMS, a new multi-corners detector, SGCDet (Soft Grouping Corner-based Detector), is proposed. Experiments show that the inference speed of SGCDet is more than two times faster than state-of-the-art corner-based models with much higher accuracy.

Corner-guided detector enjoys potential ability to yield precise bounding boxes. However, unreliable corner pairs, generated by heuristic grouping guidance, hinder the development of this detector. In this paper, we propose a novel corner grouping algorithm, termed as Corner Affinity, to significantly boost the reliability and robustness of corner grouping. The proposed Corner Affinity is a couple of two interactional factors, namely, 1) the structure affinity (SA), applying to generate preliminary corner pairs through the corresponding object's shallow construction information. 2) the contexts affinity (CA), running as optimizing corner pairs via embedding deeper semantic features of affiliated instances. Equipped with the Corner Affinity, a detector can produce high-quality bounding boxes upon preferable paired corner keypoints. Experimental results show the superiority of our design on multiple benchmark datasets. Specifically, for CornerNet baseline, the proposed Corner Affinity brings AP boostings of 5.8% on COCO, 35.8% on Citypersons, and 17.2% on UCAS-AOD without bells and whistles.

Aircraft detection in synthetic aperture radar (SAR) images plays a significant role in dynamic monitoring and national security. Previous methods have difficulty in obtaining the desirable detection performance due to the interference of complex scenes and diversity of aircraft sizes. In order to solve these problems, we propose an innovative scattering feature relation network (SFR-Net) in this article. First, considering that the strong scattering points of the aircraft in SAR images are usually discrete, we leverage the proposed scattering point relation module to fulfill the analysis and correlation of scattering points. By enhancing the characteristics and relationships among the scattering points, this method is beneficial to guarantee the completeness of aircraft detection results. Second, we design a salient fusion module to adaptively aggregate the features from different layers of SFR-Net with rich semantic information and plentiful details, which can highlight the significant objects with different sizes and enhance the distinguishable features. Third, to reduce the false alarm and improve the localization accuracy, the contextual feature attention is presented to capture the global spatial and semantic information with a large receptive field. Overall, the SFR-Net is designed based on the SAR imaging mechanism and the scattering characteristics of aircrafts. The extensive experiments are conducted on the SAR aircraft detection dataset (AIRD) from the Gaofen-3 satellite to demonstrate the effectiveness of the SFR-Net and also illustrate that our method achieves state-of-the-art performance.

Recently, deep-learning methods have been successfully applied to the ship detection in the synthetic aperture radar (SAR) images. It is still a great challenge to detect multiscale SAR ships due to the broad diversity of the scales and the strong interference of the inshore background. Most prevalent approaches are based on the anchor mechanism that uses the predefined anchors to search the possible regions containing objects. However, the anchor settings have a great impact on their detection performance as well as the generalization ability. Furthermore, considering the sparsity of the ships, most anchors are redundant and will lead to the computation increase. In this article, a novel detection method named feature balancing and refinement network (FBR-Net) is proposed. First, our method eliminates the effect of anchors by adopting a general anchor-free strategy that directly learns the encoded bounding boxes. Second, we leverage the proposed attention-guided balanced pyramid to balance semantically the multiple features across different levels. It can help the detector learn more information about the small-scale ships in complex scenes. Third, considering the SAR imaging mechanism, the interference near the ship boundary with the similar scattering power probably affects the localization accuracy because of feature misalignment. To tackle the localization issue, a feature-refinement module is proposed to refine the object features and guide the semantic enhancement. Finally, extensive experiments are conducted to show the effectiveness of our FBR-Net compared with the general anchor-free baseline. The detection results on the SAR ship detection dataset (SSDD) and AIR-SARShip-1.0 dataset illustrate that our method achieves the state-of-the-art performance.

Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users’ emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexiconbased strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches.