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“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Learning Deconvolution Network for Semantic Segmentation

Retinal vessel image is an important biological information that can be used for personal identification in the social security domain, and for disease diagnosis in the medical domain. While automatic vessel image segmentation is essential, it is also a challenging task because the retinal vessels have complex topological structures, and the retinal vessels vary in size and shape. In recent years, image segmentation based on the deep learning technique has become a mainstream method. Unfortunately, the existing methods cannot make the best use of the global information, and the model complexity is high. In this article, a convolutional neural network integrated with the attention mechanism is proposed. The overall network structure consists of a basic U-Net and an attention module, and the latter is used to capture global information and to enhance features by placing it in the process of feature fusion. Experiment results on five public datasets show that the proposed scheme outperforms other existing mainstream approaches, and most of the performance indicators are in the leading positions. More importantly, the proposed method has a significant reduction in the number of parameters.

Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation

As a cross-cutting field between ocean development and multi-robot system (MRS), the underwater multi-robot system (UMRS) has gained increasing attention from researchers and engineers in recent decades. In this paper, we present a comprehensive survey of cooperation issues, one of the key components of UMRS, from the perspective of the emergence of new functions. More specifically, we categorize the cooperation in terms of task-space, motion-space, measurement-space, as well as their combination. Further, we analyze the architecture of UMRS from three aspects, i.e., the performance of the individual underwater robot, the new functions of underwater robots, and the technical approaches of MRS. To conclude, we have discussed related promising directions for future research. This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.

A Survey of Underwater Multi-Robot Systems

Semantic segmentation is a challenging problem in computer vision. Many applications, such as autonomous driving and robot navigation with urban road scene, need accurate and efficient segmentation. Most state-of-the-art methods focus on accuracy, rather than efficiency. In this paper, we propose a more efficient neural network architecture, which has fewer parameters, for semantic segmentation in the urban road scene. An asymmetric encoder–decoder structure based on ResNet is used in our model. In the first stage of encoder, we use continuous factorized block to extract low-level features. Continuous dilated block is applied in the second stage, which ensures that the model has a larger view field, while keeping the model small-scale and shallow. The down sampled features from encoder are up sampled with decoder to the same-size output as the input image and the details refined. Our model can achieve end-to-end and pixel-to-pixel training without pretraining from scratch. The parameters of our model are only $0.2M$ , $100 \times$ less than those of others such as SegNet, etc. Experiments are conducted on five public road scene datasets (CamVid, CityScapes, Gatech, KITTI Road Detection, and KITTI Semantic Segmentation), and the results demonstrate that our model can achieve better performance.

Fast Semantic Segmentation for Scene Perception

Due to the existence of unfavorable factors such as turbid water quality and target occlusion, it is difficult to obtain valid data of target features. Due to the repeated calculation of similar data, the real-time performance of the algorithm is poor. In view of the above problems, this paper proposes a multi-AUV collaborative target recognition method based on transfer-reinforcement learning. The features of the target information which is collected by multi-AUV are fused based on wavelet transformation and affine invariance. The similarity of features is calculated by Mahalanobis distance and the learning model is selected autonomously based on the similarity threshold. Based on the Q-learning reinforcement learning model, the target information under the interference environment is trained intensively, and the effective features are extracted and stored in the source domain, which can reduce the impact of the environmental interference on the target recognition. The feature transfer learning model based on deep confidence network transfers the feature data of the source domain to the target domain, reducing the repeated calculation of similar data, and then ensuring the real-time performance of the algorithm. Simulation experiments are conducted in the SUN dataset under five underwater environments (turbid water, target occlusion, insufficient light, complex background, and overlapping targets), and the results demonstrate that the proposed model achieves better performance.

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Multi-AUV Collaborative Target Recognition Based on Transfer-Reinforcement Learning

Face sketch synthesis, as a key technique for solving face sketch recognition, has made considerable progress in recent years. Due to the difference of modality between face photo and face sketch, traditional exemplar-based methods often lead to missed texture details and deformation while synthesizing sketches. And limited to the local receptive field, Convolutional Neural Networks-based methods cannot deal with the interdependence between features well, which makes the constraint of facial features insufficient; as such, it cannot retain some details in the synthetic image. Moreover, the deeper the network layer is, the more obvious the problems of gradient disappearance and explosion will be, which will lead to instability in the training process. Therefore, in this paper, we propose a multi-scale gradients self-attention residual learning framework for face photo-sketch transformation that embeds a self-attention mechanism in the residual block, making full use of the relationship between features to selectively enhance the characteristics of specific information through self-attention distribution. Simultaneously, residual learning can keep the characteristics of the original features from being destroyed. In addition, the problem of instability in GAN training is alleviated by allowing discriminator to become a function of multi-scale outputs of the generator in the training process. Based on cycle framework, the matching between the target domain image and the source domain image can be constrained while the mapping relationship between the two domains is established so that the tasks of face photo-to-sketch synthesis (FP2S) and face sketch-to-photo synthesis (FS2P) can be achieved simultaneously. Both Image Quality Assessment (IQA) and experiments related to face recognition show that our method can achieve state-of-the-art performance on the public benchmarks, whether using FP2S or FS2P.

Multi-Scale Gradients Self-Attention Residual Learning for Face Photo-Sketch Transformation

Compared with traditional imaging, the light field contains more comprehensive image information and higher image quality. However, the available data for light field reconstruction are limited, and the repeated calculation of data seriously affects the accuracy and the real-time performance of multiperspective light field reconstruction. To solve the problems, this paper proposes a multiperspective light field reconstruction method based on transfer reinforcement learning. Firstly, the similarity measurement model is established. According to the similarity threshold of the source domain and the target domain, the reinforcement learning model or the feature transfer learning model is autonomously selected. Secondly, the reinforcement learning model is established. The model uses multiagent (i.e., multiperspective) Q-learning to learn the feature set that is most similar to the target domain and the source domain and feeds it back to the source domain. This model increases the capacity of the source-domain samples and improves the accuracy of light field reconstruction. Finally, the feature transfer learning model is established. The model uses PCA to obtain the maximum embedding space of source-domain and target-domain features and maps similar features to a new space for label data migration. This model solves the problems of multiperspective data redundancy and repeated calculations and improves the real-time performance of maneuvering target recognition. Extensive experiments on PASCAL VOC datasets demonstrate the effectiveness of the proposed algorithm against the existing algorithms.

Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning.

It is difficult to reconstruct the complete light field, and the reconstructed light field can only recognize specific fixed targets. These have limited the applications of the light field in pract...

https://journals.sagepub.com/doi/pdf/10.1177/1550147719870657

Lei Cai

Papers

Fast Semantic Segmentation for Scene Perception

Multi-AUV Collaborative Target Recognition Based on Transfer-Reinforcement Learning

Multi-Scale Gradients Self-Attention Residual Learning for Face Photo-Sketch Transformation

Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning.

Maneuvering target recognition method based on multi-perspective light field reconstruction: