Impact cratering process is the major geologic activity on the surface of the Moon, and the spatial distribution and size-frequency distribution of lunar craters are indicative to the bombardment history of the Solar System. The substantial efforts on the development of automated crater detection algorithms (CDAs) have been carried out on the images from the remote sensing observations. Recently, CDAs via convolutional neural network (CNN) on digital elevation model (DEM) has been developed as it can combine the discrimination ability of CNN with the robust characteristic of the DEM data. However, most of the existing algorithms adopt a traditional two-stage detection pipeline including an edge segmentation and a template matching step. In this paper, we attempt to reduce the gap between the existing DEM-based CDAs and the advanced CNN methods for object detection, and propose a complete workflow including an end-to-end deep learning pipeline for lunar crater detection, in particular for craters smaller than 50 km in diameter. Based on the workflow, we benchmark nine representative CNN models involving three popular types of detection architectures. Moreover, we elaborate on the practical application of the proposed workflow, and provide an example method to demonstrate the performance advantage in terms of the precision ($82.97\%$) and recall ($79.39\%$). Furthermore, we develop a crater verification tool to manually validate the detection results, and the visualization results show that our detected craters are reasonable and can be used as a supplement to the existing hand-labeled datasets.

Context prediction plays a crucial role in implementing autonomous driving applications. As one of important context-prediction tasks, crowd-and-vehicle counting is critical for achieving real-time traffic and crowd analysis, consequently facilitating decision-making processes for autonomous vehicles. However, the completion of crowd-and-vehicle counting also faces challenges, such as large-scale variations, imbalanced data distribution, and insufficient local patterns. To tackle these challenges, we put forth a novel frequency feature pyramid network (FFPNet) in this paper. Our proposed FFPNet extracts the multi-scale information by frequency feature pyramid module, which can tackle the issue of large-scale variations. Meanwhile, the frequency feature pyramid module uses different frequency branches to obtain different scale information. We also adopt the attention mechanism to strength the extraction of different scale information. Moreover, we devise a novel loss function, namely global-local consistency loss, to address the existing problems of imbalanced data distribution and insufficient local patterns. Furthermore, we conduct extensive experiments on six datasets to evaluate our proposed FFPNet. It is worth mentioning that we also construct a novel crowd-and-vehicle dataset (CROVEH), which is the only dataset that contains both crowd-and-vehicle annotations. The experimental results show that FFPNet achieves the best performance on different backbones, e.g., 52.69 mean absolute error (MAE) on P2PNet with FFP module. The codes are available at: https://github.com/MUST-AI-Lab/FFPNet .

Indoor localization and navigation have become an increasingly important problem in both industry and academia with the widespread use of mobile smart devices and the development of network techniques. The Wi-Fi-based technology shows great potential for applications due to the ubiquitous Wi-Fi infrastructure in public indoor environments. Most existing approaches use trilateration or machine learning methods to predict locations from a set of annotated Wi-Fi observations. However, annotated data are not always readily available. In this paper, we propose a robot-aided data collection strategy to obtain the limited but high-quality labeled data and a large amount of unlabeled data. Furthermore, we design two deep learning models based on a variational autoencoder for the localization and navigation tasks, respectively. To make full use of the collected data, a hybrid learning approach is developed to train the models by combining supervised, unsupervised and semi-supervised learning strategies. Extensive experiments suggest that our approach enables the models to learn effective knowledge from unlabeled data with incremental improvements, and it can achieve promising localization and navigation performance in a complex indoor environment with obstacles.

Deep multitask learning for face analysis has received increasing attentions. From literature, most existing methods focus on optimizing a main task by jointly learning several auxiliary tasks. It is challenging to consider the performance of each task in a multitask framework due to the following reasons: 1) different face tasks usually rely on different levels of semantic features; 2) each task has different learning convergence rate, which could affect the whole performance when joint training; and 3) multitask model needs rich label information for efficient training, but existing facial datasets provide limited annotations. To address these issues, we propose a task-oriented feature-fused network (TFN) for simultaneously solving face detection, landmark localization, and attribute analysis. In this network, a task-oriented feature-fused block is designed to learn task-specific feature combinations; then, an alternative multitask training scheme is presented to optimize each task with considering of their different learning capacities. We also present a large-scale face dataset called JFA in support of proposed method, which provides multivariate labels, including face bounding box, 68 facial landmarks, and 3 attribute labels (i.e., apparent age, gender, and ethnicity). The experimental results suggest that the TFN outperforms several multitask models on the JFA dataset. Furthermore, our approach achieves competitive performances on WIDER FACE and 300W dataset, and obtains state-of-the-art results for gender recognition on the MORPH II dataset.

The recent studies for face alignment have involved developing an isolated algorithm on well-cropped face images. It is difficult to obtain the expected input by using an off-the-shelf face detector in practical applications. In this paper, we attempt to bridge between face detection and face alignment by establishing a novel joint multi-task model, which allows us to simultaneously detect multiple faces and their landmarks on a given scene image. In contrast to the pipeline-based framework by cascading separate models, we aim to propose an end-to-end convolutional network by sharing and transform feature representations between the task-specific modules. To learn a robust landmark estimator for unconstrained face alignment, three types of context enhanced block are designed to encode feature maps with multi-level context, multi-scale context, and global context, respectively. In the post-processing step, we develop a shape reconstruction algorithm based on Point Distribution Model (PDM) to refine the landmark outliers. Extensive experiments demonstrate that our results are robust for the landmark location task and insensitive to the location of estimated face regions. Furthermore, our method significantly outperforms recent state-of-the-art methods on several challenging datasets including 300W, AFLW, and COFW.