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HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird’s-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We annotate the depth levels of transparent glass walls by propagating the context depth values within neighboring flat areas, and the glass segmentation mask and instance level line segments of glass edges are also provided. On the other hand, a tailored monocular depth estimation method is proposed to fully activate the glass wall contextual understanding. First, we propose to exploit the glass structure context by incorporating the structural prior knowledge embedded in glass boundary line segment detections. Furthermore, to make our method adaptive to scenes without structure context where the glass boundary is either absent in the image or too narrow to be recognized, we propose to derive a reflection context by utilizing the depth reliable points sampled according to the variance between two depth estimations from different resolutions. High-resolution depth is thus estimated by the weighted summation of depths by those reliable points. Extensive experiments are conducted to evaluate the effectiveness of the proposed dual context design. Superior performances of our method is also demonstrated by comparing with state-of-the-art methods. We present the first feasible solution for monocular depth estimation in the presence of glass walls, which can be widely adopted in autonomous navigation.

In this article, finite-time synchronization is investigated for fractional-order fuzzy time-varying coupled neural networks subject to reaction–diffusion by establishing a new framework under fuzzy-based feedback control and fuzzy-based adaptive control. For the considered networks, we put forward an innovative graph-theory-based time-varying Lyapunov function. To overcome the difficulty of estimating the fractional derivative of this function, this article proposes a novel fractional derivative rule. Through graph theory and the Lyapunov method, several finite-time synchronous criteria are obtained for the considered networks, and the estimation of the settling time is derived. Finally, the numerical results are shown to demonstrate the practicability of the given results.

Taking photos of digital screens often produces color-distorting moire patterns caused by inconsistency between the color filter array of cameras and the sub-pixel layout of screens, which severely degrades the quality of photos. Most existing demoireing methods employ multi-stream network architecture to simultaneously process the same moire image with different resolutions, but they neglect the complementarity among different resolutions. In this paper, we propose a novel moire removal model to address this issue. Unlike the existing multi-stream based approaches, in this model, we present a progressive texture complementation block to exploit the complementary information from different resolutions in order to progressively remove moire textures and restore image content. Additionally, we propose a residual moire removal block, in which the depthwise separable convolution is utilized to remove moire from image while reducing computation overhead. This block also includes a local color correction structure, which is used to correct color shifts presented in the moire images. Experimental results on two public datasets show that our method outperforms state-of-the-art methods. Besides, the quantity of parameters and FLOPs of our model are tens of times fewer than the off-the-shelf models. Furthermore, our network framework can adapt well to another low-level vision task, rain removal, in which our model also achieves state-of-the-art performance.

Deep reinforcement learning has achieved great success in laser-based collision avoidance works because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to deploy for a large scale of robots but also demonstrate unsatisfactory robustness towards the complex obstacles, including irregular obstacles, e.g., tables, chairs, and shelves, as well as complex ground and special materials. In this paper, we propose a novel monocular camera-based complex obstacle avoidance framework. Particularly, we innovatively transform the captured RGB images to pseudo-laser measurements for efficient deep reinforcement learning. Compared to the traditional laser measurement captured at a certain height that only contains one-dimensional distance information away from the neighboring obstacles, our proposed pseudo-laser measurement fuses the depth and semantic information of the captured RGB image, which makes our method effective for complex obstacles. We also design a feature extraction guidance module to weight the input pseudo-laser measurement, and the agent has more reasonable attention for the current state, which is conducive to improving the accuracy and efficiency of the obstacle avoidance policy. Besides, we adaptively add the synthesized noise to the laser measurement during the training stage to decrease the sim-to-real gap and increase the robustness of our model in the real environment. Finally, the experimental results show that our framework achieves state-of-the-art performance in several virtual and real-world scenarios.

Existing image aesthetics assessment methods mainly rely on the visual features of images but ignore their rich semantics. Nowadays, with the widespread application of social media, the comments corresponding to images in the form of texts can be easily accessed and provide rich semantic information, which can be utilized to effectively complement image features. This paper proposes a comment-guided semantics-aware image aesthetics assessment method, which is built upon a multi-task learning framework for image aesthetics prediction and comment-guided semantics classification. To assist image aesthetics assessment, we first model the semantics of an image as the topic features of its corresponding comments using Latent Dirichlet Allocation. We then propose a two-stream multitask learning framework for both topic feature prediction and aesthetic score distribution prediction. Topic feature prediction task enables to infer the semantics from images, since the comments are usually unavailable during inference and comment-guided semantics can only serve as supervision during training. We further propose to deeply fuse aesthetics and semantic features using a layerwise feature fusion method. Experimental results demonstrate that the proposed method outperforms state-of-the-art image aesthetics assessment methods.

Integrating low-level edge features has been proven to be effective in preserving clear boundaries of salient objects. However, the locality of edge features makes it difficult to capture globally salient edges, leading to distraction in the final predictions. To address this problem, we propose to produce distraction-free edge features by incorporating cross-scale holistic interdependencies between high-level features. In particular, we first formulate our edge features extraction process as a boundary-filling problem. In this way, we enforce edge features to focus on closed boundaries instead of those disconnected background edges. Second, we propose to explore cross-scale holistic contextual connections between every position pair of high-level feature maps regardless of their distances across different scales. It selectively aggregates features at each position based on its connections to all the others, simulating the “contrast” stimulus of visual saliency. Finally, we present a complementary features integration module to fuse low- and high-level features according to their properties. Experimental results demonstrate our proposed method outperforms previous state-of-the-art methods on the benchmark datasets, with the fast inference speed of 30 FPS on a single GPU.

Background:Various pharmacokinetic (PK) equations and software have been developed to individualize vancomycin dosing. However, the benefit of using any PK information to guide vancomycin dosing has not been fully elucidated. Objective:To appraise available evidence on the effectiveness and safety of individualized vancomycin dosing via PK tools. Methods:PubMed, EMBASE, the Cochrane Library, and 2 Chinese literature databases were searched through August 1, 2019. Randomized controlled trials (RCTs) and cohort studies that reported the PK and clinical outcomes of individualized vancomycin dosing versus empirical dosing were included. Pooled risk ratios (RRs) and mean differences were calculated for dichotomous and continuous outcomes, respectively. Results:A total of 21 studies involving 4346 patients were finally included, of which 3 were RCTs and 18 were cohort studies. Meta-analysis revealed that PK-guided vancomycin dosing significantly increased the attainment of target trough concentration (RR = 1.59; 95% CI = 1.49-1.70) and decreased the incidence of nephrotoxicity (RR = 0.57; 95% CI = 0.46-0.71). Additionally, the available evidence showed that target area under the curve/minimum inhibitory concentration attainment rate and time to target concentration could improve. However, the evidence on clinical outcomes was scarce, and no significant differences were detected in clinical response rate, microbiological eradication rate, mortality, and length of hospital stay between PK-guided vancomycin dosing and empirical dosing strategies. Conclusion and Relevance:Individualized vancomycin dosing via PK tools significantly increases the attainment of target trough concentration and decreases the incidence of nephrotoxicity. Evidence on clinical effectiveness was limited and showed no significant benefit. Further well-designed studies are warranted to assess its clinical effectiveness and inform routine care.

•A 12-week, remote, YouTube video-delivered intervention improves free-living physical activity.•Muscle-strengthening activity improved after the 12-week, home-based, exercise intervention.•Home-based high-intensity interval training and resistance-training workouts improved autonomous exercise motivation.•The intervention improved sleep quality (sleep efficiency) during the COVID-19 pandemic.•A remote, video-delivered exercise program improved perceived physical activity and exercise barriers.