Terrestrial water storage changes over the Pearl River Basin from GRACE and connections with Pacific climate variability

AbstractTime-variable gravity data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are used to study terrestrial water storage (TWS) changes over the Pearl River Basin (PRB) for the period 2003–Nov. 2014. TWS estimates from GRACE generally show good agreement with those from two hydrological models GLDAS and WGHM. But they show different capability of detecting significant TWS changes over the PRB. Among them, WGHM is likely to underestimate the seasonal variability of TWS, while GRACE detects long-term water depletions over the upper PRB as was done by hydrological models, and observes significant water increases around the Longtan Reservoir (LTR) due to water impoundment. The heavy drought in 2011 caused by the persistent precipitation deficit has resulted in extreme low surface runoff and water level of the LTR. Moreover, large variability of summer and autumn precipitation may easily trigger floods and droughts in the rainy season in the PRB, especially for summer, as a high correlation of 0.89 was found between precipitation and surface runoff. Generally, the PRB TWS was negatively correlated with El Niño-Southern Oscillation (ENSO) events. However, the modulation of the Pacific Decadal Oscillation (PDO) may impact this relationship, and the significant TWS anomaly was likely to occur in the peak of PDO phase as they agree well in both of the magnitude and timing of peaks. This indicates that GRACE-based TWS could be a valuable parameter for studying climatic influences in the PRB

Insignificant shadow detection for video segmentation

To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance

Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easily discriminated as done on the source domain anymore. Instead, ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation. By considering the same class prototypes as positives and other class prototypes as negatives to achieve class-centered distribution alignment, ProCA achieves state-of-the-art performance on classical domain adaptation tasks, {\em i.e., GTA5 $\to$ Cityscapes \text{and} SYNTHIA $\to$ Cityscapes}. Code is available at \href{https://github.com/jiangzhengkai/ProCA}{ProCA

You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction

Challenging illumination conditions (low-light, under-exposure and over-exposure) in the real world not only cast an unpleasant visual appearance but also taint the computer vision tasks. After camera captures the raw-RGB data, it renders standard sRGB images with image signal processor (ISP). By decomposing ISP pipeline into local and global image components, we propose a lightweight fast Illumination Adaptive Transformer (IAT) to restore the normal lit sRGB image from either low-light or under/over-exposure conditions. Specifically, IAT uses attention queries to represent and adjust the ISP-related parameters such as colour correction, gamma correction. With only ~90k parameters and ~0.004s processing speed, our IAT consistently achieves superior performance over SOTA on the current benchmark low-light enhancement and exposure correction datasets. Competitive experimental performance also demonstrates that our IAT significantly enhances object detection and semantic segmentation tasks under various light conditions. Training code and pretrained model is available at https://github.com/cuiziteng/Illumination-Adaptive-Transformer.Comment: 23 page