Distribution Fitting for Combating Mode Collapse in Generative Adversarial Networks

Mode collapse is a significant unsolved issue of generative adversarial networks. In this work, we examine the causes of mode collapse from a novel perspective. Due to the nonuniform sampling in the training process, some sub-distributions may be missed when sampling data. As a result, even when the generated distribution differs from the real one, the GAN objective can still achieve the minimum. To address the issue, we propose a global distribution fitting (GDF) method with a penalty term to confine the generated data distribution. When the generated distribution differs from the real one, GDF will make the objective harder to reach the minimal value, while the original global minimum is not changed. To deal with the circumstance when the overall real data is unreachable, we also propose a local distribution fitting (LDF) method. Experiments on several benchmarks demonstrate the effectiveness and competitive performance of GDF and LDF

Unified Chinese License Plate Detection and Recognition with High Efficiency

Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP images as a supplement to the existing public benchmarks. The images are mainly captured with electronic monitoring systems with detailed annotations. To our knowledge, CRPD is the largest public multi-objective Chinese LP dataset with annotations of vertices. With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline. The network is end-to-end trainable with totally real-time inference efficiency (30 fps with 640p). The experiments on several public benchmarks demonstrate that our method has reached competitive performance. The code and dataset will be publicly available at https://github.com/yxgong0/CRPD

LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment

3D panoptic segmentation is a challenging perception task that requires both semantic segmentation and instance segmentation. In this task, we notice that images could provide rich texture, color, and discriminative information, which can complement LiDAR data for evident performance improvement, but their fusion remains a challenging problem. To this end, we propose LCPS, the first LiDAR-Camera Panoptic Segmentation network. In our approach, we conduct LiDAR-Camera fusion in three stages: 1) an Asynchronous Compensation Pixel Alignment (ACPA) module that calibrates the coordinate misalignment caused by asynchronous problems between sensors; 2) a Semantic-Aware Region Alignment (SARA) module that extends the one-to-one point-pixel mapping to one-to-many semantic relations; 3) a Point-to-Voxel feature Propagation (PVP) module that integrates both geometric and semantic fusion information for the entire point cloud. Our fusion strategy improves about 6.9% PQ performance over the LiDAR-only baseline on NuScenes dataset. Extensive quantitative and qualitative experiments further demonstrate the effectiveness of our novel framework. The code will be released at https://github.com/zhangzw12319/lcps.git.Comment: Accepted as ICCV 2023 pape

In situ extracting organic-bound calcium:A novel approach to mitigating organic fouling in forward osmosis treating wastewater via gradient diffusion thin-films

Forward osmosis (FO) has gained increasing interests in wastewater treatment and reclamation. However, membrane fouling has become one major obstacle hindering FO application. A novel mitigation approach for FO membrane fouling via in situ extracting Ca 2+ binding with the organic foulants using the gradient diffusion thin-films (DGT) was proposed in this study. The DGT could effectively adsorb the Ca 2+ binding with the sodium alginate via the chelation of the Chelex functional groups, and its adsorption amount of Ca 2+ correspondingly increased as a function of the Ca 2+ concentration in the feed solution. Owing to the extraction of Ca 2+ from the fouling layer by the DGT, the FO membrane fouling was effectively mitigated evident by significant enhancement of water flux, and at the same time, foulants became easily removed by physical cleaning. The alleviation of FO membrane fouling by the DGT could be attributed to the fact that the structure of the fouling layer became more porous and looser after in situ removing Ca 2+ from the alginate-Ca 2+ gel networks. The feasibility of fouling control strategy via in situ removing Ca 2+ binding with the foulants in the fouling layer was demonstrated, which provides new insights into fouling control mechanisms during FO treating wastewater. </p