Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes

Semantic segmentation, a pixel-level vision task, is developed rapidly by using convolutional neural networks (CNNs). Training CNNs requires a large amount of labeled data, but manually annotating data is difficult. For emancipating manpower, in recent years, some synthetic datasets are released. However, they are still different from real scenes, which causes that training a model on the synthetic data (source domain) cannot achieve a good performance on real urban scenes (target domain). In this paper, we propose a weakly supervised adversarial domain adaptation to improve the segmentation performance from synthetic data to real scenes, which consists of three deep neural networks. To be specific, a detection and segmentation ("DS" for short) model focuses on detecting objects and predicting segmentation map; a pixel-level domain classifier ("PDC" for short) tries to distinguish the image features from which domains; an object-level domain classifier ("ODC" for short) discriminates the objects from which domains and predicts the objects classes. PDC and ODC are treated as the discriminators, and DS is considered as the generator. By adversarial learning, DS is supposed to learn domain-invariant features. In experiments, our proposed method yields the new record of mIoU metric in the same problem.Comment: To appear at TI

PCC Net: Perspective Crowd Counting via Spatial Convolutional Network

Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion. Many methods only focus on the local appearance features and they cannot handle the aforementioned challenges. In order to tackle them, we propose a Perspective Crowd Counting Network (PCC Net), which consists of three parts: 1) Density Map Estimation (DME) focuses on learning very local features for density map estimation; 2) Random High-level Density Classification (R-HDC) extracts global features to predict the coarse density labels of random patches in images; 3) Fore-/Background Segmentation (FBS) encodes mid-level features to segments the foreground and background. Besides, the DULR module is embedded in PCC Net to encode the perspective changes on four directions (Down, Up, Left and Right). The proposed PCC Net is verified on five mainstream datasets, which achieves the state-of-the-art performance on the one and attains the competitive results on the other four datasets. The source code is available at https://github.com/gjy3035/PCC-Net.Comment: accepted by IEEE T-CSV

Limitation of SDMA in Ultra-Dense Small Cell Networks

Benefitting from multi-user gain brought by multi-antenna techniques, space division multiple access (SDMA) is capable of significantly enhancing spatial throughput (ST) in wireless networks. Nevertheless, we show in this letter that, even when SDMA is applied, ST would diminish to be zero in ultra-dense networks (UDN), where small cell base stations (BSs) are fully densified. More importantly, we compare the performance of SDMA, single-user beamforming (SU-BF) (one user is served in each cell) and full SDMA (the number of served users equals the number of equipped antennas). Surprisingly, it is shown that SU-BF achieves the highest ST and critical density, beyond which ST starts to degrade, in UDN. The results in this work could shed light on the fundamental limitation of SDMA in UDN

Network Densification in 5G: From the Short-Range Communications Perspective

Besides advanced telecommunications techniques, the most prominent evolution of wireless networks is the densification of network deployment. In particular, the increasing access points/users density and reduced cell size significantly enhance spatial reuse, thereby improving network capacity. Nevertheless, does network ultra-densification and over-deployment always boost the performance of wireless networks? Since the distance from transmitters to receivers is greatly reduced in dense networks, signal is more likely to be propagated from long- to short-range region. Without considering short-range propagation features, conventional understanding of the impact of network densification becomes doubtful. With this regard, it is imperative to reconsider the pros and cons brought by network densification. In this article, we first discuss the short-range propagation features in densely deployed network and verify through experimental results the validity of the proposed short-range propagation model. Considering short-range propagation, we further explore the fundamental impact of network densification on network capacity, aided by which a concrete interpretation of ultra-densification is presented from the network capacity perspective. Meanwhile, as short-range propagation makes interference more complicated and difficult to handle, we discuss possible approaches to further enhance network capacity in ultra-dense wireless networks. Moreover, key challenges are presented to suggest future directions.Comment: submitted to IEEE Commun. Ma

The Impact of Antenna Height Difference on the Performance of Downlink Cellular Networks

Capable of significantly reducing cell size and enhancing spatial reuse, network densification is shown to be one of the most dominant approaches to expand network capacity. Due to the scarcity of available spectrum resources, nevertheless, the over-deployment of network infrastructures, e.g., cellular base stations (BSs), would strengthen the inter-cell interference as well, thus in turn deteriorating the system performance. On this account, we investigate the performance of downlink cellular networks in terms of user coverage probability (CP) and network spatial throughput (ST), aiming to shed light on the limitation of network densification. Notably, it is shown that both CP and ST would be degraded and even diminish to be zero when BS density is sufficiently large, provided that practical antenna height difference (AHD) between BSs and users is involved to characterize pathloss. Moreover, the results also reveal that the increase of network ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and network performance, we further study the critical density, under which ST could be maximized under the CP constraint. Through a special case study, it follows that the critical density is inversely proportional to the square of AHD. The results in this work could provide helpful guideline towards the application of network densification in the next-generation wireless networks.Comment: conference submission - Mar. 201

MISO in Ultra-Dense Networks: Balancing the Tradeoff between User and System Performance

With over-deployed network infrastructures, network densification is shown to hinder the improvement of user experience and system performance. In this paper, we adopt multi-antenna techniques to overcome the bottleneck and investigate the performance of single-user beamforming, an effective method to enhance desired signal power, in small cell networks from the perspective of user coverage probability (CP) and network spatial throughput (ST). Pessimistically, it is proved that, even when multi-antenna techniques are applied, both CP and ST would be degraded and even asymptotically diminish to zero with the increasing base station (BS) density. Moreover, the results also reveal that the increase of ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and system performance, we further study the critical density, under which ST could be maximized under the CP constraint. Accordingly, the impact of key system parameters on critical density is quantified via the derived closed-form expression. Especially, the critical density is shown to be inversely proportional to the square of antenna height difference between BSs and users. Meanwhile, single-user beamforming, albeit incapable of improving CP and ST scaling laws, is shown to significantly increase the critical density, compared to the single-antenna regime.Comment: for journal submissio

PartsNet: A Unified Deep Network for Automotive Engine Precision Parts Defect Detection

Defect detection is a basic and essential task in automatic parts production, especially for automotive engine precision parts. In this paper, we propose a new idea to construct a deep convolutional network combining related knowledge of feature processing and the representation ability of deep learning. Our algorithm consists of a pixel-wise segmentation Deep Neural Network (DNN) and a feature refining network. The fully convolutional DNN is presented to learn basic features of parts defects. After that, several typical traditional methods which are used to refine the segmentation results are transformed into convolutional manners and integrated. We assemble these methods as a shallow network with fixed weights and empirical thresholds. These thresholds are then released to enhance its adaptation ability and realize end-to-end training. Testing results on different datasets show that the proposed method has good portability and outperforms the state-of-the-art algorithms.Comment: 2nd International Conference on Computer Science and Artificial Intelligence (CSAI 2018

NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization

In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc. Many Convolutional Neural Networks (CNN) are designed for tackling this task. However, currently released datasets are so small-scale that they can not meet the needs of the supervised CNN-based algorithms. To remedy this problem, we construct a large-scale congested crowd counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (0~20,033). Besides, a benchmark website is developed for impartially evaluating the different methods, which allows researchers to submit the results of the test set. Based on the proposed dataset, we further describe the data characteristics, evaluate the performance of some mainstream state-of-the-art (SOTA) methods, and analyze the new problems that arise on the new data. What's more, the benchmark is deployed at \url{https://www.crowdbenchmark.com/}, and the dataset/code/models/results are available at \url{https://gjy3035.github.io/NWPU-Crowd-Sample-Code/}.Comment: Accepted by T-PAM

Modeling and Analysis of SCMA Enhanced D2D and Cellular Hybrid Network

Sparse code multiple access (SCMA) has been recently proposed for the future wireless networks, which allows non-orthogonal spectrum resource sharing and enables system overloading. In this paper, we apply SCMA into device-to-device (D2D) communication and cellular hybrid network, targeting at using the overload feature of SCMA to support massive device connectivity and expand network capacity. Particularly, we develop a stochastic geometry based framework to model and analyze SCMA, considering underlaid and overlaid mode. Based on the results, we analytically compare SCMA with orthogonal frequency-division multiple access (OFDMA) using area spectral efficiency (ASE) and quantify closed-form ASE gain of SCMA over OFDMA. Notably, it is shown that system ASE can be significantly improved using SCMA and the ASE gain scales linearly with the SCMA codeword dimension. Besides, we endow D2D users with an activated probability to balance cross-tier interference in the underlaid mode and derive the optimal activated probability. Meanwhile, we study resource allocation in the overlaid mode and obtain the optimal codebook allocation rule. It is interestingly found that the optimal SCMA codebook allocation rule is independent of cellular network parameters when cellular users are densely deployed. The results are helpful in the implementation of SCMA in the hybrid system.Comment: submitted to IEEE Trans. Commu

Access Points in the Air: Modeling and Optimization of Fixed-Wing UAV Network

Fixed-wing unmanned aerial vehicles (UAVs) are of great potential to serve as aerial access points (APs) owing to better aerodynamic performance and longer flight endurance. However, the inherent hovering feature of fixed-wing UAVs may result in discontinuity of connections and frequent handover of ground users (GUs). In this work, we model and evaluate the performance of a fixed-wing UAV network, where UAV APs provide coverage to GUs with millimeter wave backhaul. Firstly, it reveals that network spatial throughput (ST) is independent of the hover radius under real-time closest-UAV association, while linearly decreases with the hover radius if GUs are associated with the UAVs, whose hover center is the closest. Secondly, network ST is shown to be greatly degraded with the over-deployment of UAV APs due to the growing air-to-ground interference under excessive overlap of UAV cells. Finally, aiming to alleviate the interference, a projection area equivalence (PAE) rule is designed to tune the UAV beamwidth. Especially, network ST can be sustainably increased with growing UAV density and independent of UAV flight altitude if UAV beamwidth inversely grows with the square of UAV density under PAE