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Even though deep reinforcement learning (DRL) has been extensively applied to quadrotor flight control to simplify parameter adjustment, it has some drawbacks in terms of control performance, such as instability and asymmetry. To address these problems, we propose an odd symmetric actor to achieve stable and symmetric control performance, and an even critic to stabilize the training process. Concretely, the bias of neural networks is eliminated, and the absolute value operation is adopted to construct the activation function. Furthermore, we devise a cascade architecture, where each module trained with DRL controls a symmetric subsystem of the quadrotor. Comparative simulations have verified the effectiveness of the proposed control scheme, which shows superiority in dealing with high-dimensional, nonlinear subsystems and disadvantage in dealing with low-dimensional, linear subsystems. [ABSTRACT FROM AUTHOR]

Colorectal polyps are known to be potential precursors to colorectal cancer. Effective polyp segmentation during colonoscopy examinations can help the clinicians accurately locate potential polyp areas, and reduce misdiagnosis and missed diagnosis. Although existing approaches have achieved significant breakthroughs in medical image segmentation, polyp segmentation is still far from being solved. This is mainly due to the following reasons: (1) most of them tend to ignore the feature misalignment issues during the feature aggregation process; and (2) few algorithms explicitly consider the impact of boundary information on the performance of polyp segmentation. To solve the above issues, we formulate a novel neural network for polyp segmentation in endoscopy images. Different from existing approaches, we explore a new paradigm to enhance multi-level feature fusion by introducing the Feature Fusion Module, which leverages the semantic offset field learned to align the multi-level feature maps for resolving the feature misalignment issue. In addition, we design an auxiliary boundary branch to focus on boundary-aware information, and thus boost the performance of boundary prediction. Specifically, the reference boundary map learned through end-to-end optimization can be considered as a complementary feature of the high-level semantics representation, and then integrated into the main branch via the Boundary Embedding Module, which in turn promotes further refinement of the prediction, especially the boundary. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in polyp segmentation. [ABSTRACT FROM AUTHOR]

It is difficult to use supervised machine-learning methods for infrared (IR) and visible (VIS) image fusion (IVF) because of the shortage of ground-truth target fusion images, and image quality and contrast control are rarely considered in existing IVF methods. In this study, we proposed a simple IVF pipeline that converts the IVF problem into a supervised binary classification problem (sharp vs. blur) and uses image enhancement techniques to improve the image quality in three locations in the pipeline. We took a biological vision consistent assumption that the sharp region contains more useful information than the blurred region. A deep binary classifier based on a convolutional neural network (CNN) was designed to compare the sharpness of the infrared region and visible regions. The output score map of the deep classifier was treated as a weight map in the weighted average fusion rule. The proposed deep binary classifier was trained using natural visible images from the MS COCO dataset, rather than images from the IVF domain (called "cross domain training" here). Specifically, our proposed pipeline contains four stages: (1) enhancing the IR and VIS input images by linear transformation and the High-Dynamic-Range Compression (HDRC) method, respectively; (2) inputting the enhanced IR and VIS images to the trained CNN classifier to obtain the weight map; and (3) using a weight map to obtain the weighted average of the enhanced IR and VIS images; and (4) using single scale Retinex (SSR) to enhance the fused image to obtain the final enhanced fusion image. Extensive experimental results on public IVF datasets demonstrate the superior performance of our proposed approach over other state-of-the-art methods in terms of both subjective visual quality and 11 objective metrics. It was demonstrated that the complementary information between the infrared and visible images can be efficiently extracted using our proposed binary classifier, and the fused image quality is significantly improved. The source code is available at https://github.com/eyob12/Deep_infrared_and_visible_image_fusion. [ABSTRACT FROM AUTHOR]

Nowadays, graph representation learning has aroused a lot of research interest, which aims to learn the latent low-dimensional representations of graph nodes, while preserving the graph structure. Based on the local smooth assumption, some existing methods have achieved significant success. However, although the structure information of data has been taken into consideration, these models fail to capture enough connectivity pattern such as high-order connections. To alleviate this issue, we propose a Graph Diffusion Network (GDN) that can dynamically preserve local and global consistency of graph. More specifically, Graph Diffusion Auto-encoder is utilized as the main framework in GDN to nonlinearly maintain global information volume. Different from simple auto-encoders, the forward propagation in our model is conducted through Graph Diffusion System which can guide the random walk of information flow to sense the high-order local relationships on graph. Furthermore, to discover a customized graph structure that reveals the similarities between nodes, the connection relationship between nodes are refined by learned metrics with the preservation of scale-free property. By the dynamically self-refining on the graph structure, it can be promoted towards learning the intrinsic node representations in a progressive way. Experimental results on node classification tasks demonstrate the effectiveness of the proposed GDN model. [ABSTRACT FROM AUTHOR]