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 pre-training from scratch. The parameters of our model are only 0.2M, 100x 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.

Underwater imaging systems have evolved into essential hardware equipment for developing and utilizing marine resources. However, the complex underwater physical environment has often led to severe quality degradation of underwater visual perception. To address these issues, we design a principal component fusion method of foreground and background to enhance an underwater image, named PCFB. Specifically, we present a color balance-guided color correction strategy to remove color distortion issues that equalize the pixel values of the a and b channels of the CIELab color model. Subsequently, we implement a percentile maximum-based contrast enhancement strategy and a multilayer transmission map estimated dehazing strategy on the color-corrected image to yield the contrast-enhanced foreground and dehazed background sub-images. Finally, we employ a principal component analysis fusion method to reconstruct a high-visibility underwater image by integrating the advantages of the foreground contrast-enhanced sub-image and the background dehazed sub-image. Comprehensive experiments on three datasets demonstrate that our PCFB surpasses state-of-the-art methods both qualitatively and quantitatively. Moreover, our PCFB exhibits outstanding generalization capabilities for addressing haze and low-light images. The code is publicly available at: https://www.researchgate.net/publication/381259520 2024-PCFB.

Although high-accuracy networks have been applied to semantic segmentation at present, their inference speeds remain slow. A trade-off between accuracy and speed is demanded for real-time applications. To approach this problem, we propose Multi-Feature Fusion Network (MFNet) with real-time efficient prediction capacity. MFNet adopts three branches (attention, semantic and spatial information) to capture low-level and high-level features. Additionally, MFNet exerts asymmetric factorized (AF) blocks to extract local and long-range features. As a result, without any pre-training or post-processing, MFNet using only 1.34 M parameters, achieves 72.1% mean intersection over union (mIoU) on the Cityscapes test set at a speed of 116 frames per second (FPS), with $512\times1024$ high resolution on a single Titan Xp graphics card. Our network’s performance stands out from other state-of-the-art networks on four datasets (Cityscapes, CamVid, KITTI, and Gatech).

Protein interactions are important in a broad range of biological processes. Traditionally, computational methods have been developed to automatically predict protein interface from hand-crafted features. Recent approaches employ deep neural networks and predict the interaction of each amino acid pair independently. However, these methods do not incorporate the important sequential information from amino acid chains and the high-order pairwise interactions. Intuitively, the prediction of an amino acid pair should depend on both their features and the information of other amino acid pairs. In this work, we propose to formulate the protein interface prediction as a 2D dense prediction problem. In addition, we propose a novel deep model to incorporate the sequential information and high-order pairwise interactions to perform interface predictions. We represent proteins as graphs and employ graph neural networks to learn node features. Then we propose the sequential modeling method to incorporate the sequential information and reorder the feature matrix. Next, we incorporate high-order pairwise interactions to generate a 3D tensor containing different pairwise interactions. Finally, we employ convolutional neural networks to perform 2D dense predictions. Experimental results on multiple benchmarks demonstrate that our proposed method can consistently improve the protein interface prediction performance.

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.

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.