How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation

Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent frames, greatly speeding up the inference phase. However, their accuracy drops significantly owing to the imprecise motion estimation and error accumulation. In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models. The experimental results on the Cityscapes dataset show that with the correction, the accuracy (mIoU) significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS. For non-rigid categories such as "human" and "object", the improvements of IoU are even higher than 18 percentage points

A global comparative study on the impact of COVID-19 policy on atmospheric nitrogen dioxide (NO₂):Evidence from remote sensing data in 2019–2022

A significant body of research has documented the profound changes in global atmospheric conditions during the COVID-19 pandemic. However, there is still an inadequate comprehensive comparison and assessment of countries before, during, and after the pandemic. Variations in restriction policies, human behaviors, and national traits lead to significant differences in how restriction policies affect atmospheric pollution. This study focuses on NO2, a pollutant with high temporal sensitivity, and utilizes the Oxford COVID-19 policy stringency index along with demographic information. Through spatial-temporal mapping, we analyzed NO2 emission fluctuations and calculated the emission changes in each country. Drawing from this analysis, we explored the relationships among these factors and found that over the span of 2019–2022, across 193 countries, global NO2 emissions displayed a distinct trajectory: initially decreasing, subsequently rebounding, and eventually fluctuating. Most countries exhibited seasonal variations in NO2 emissions. Additionally, the study uncovered a correlation between the stringency of COVID-19 policies and the reduction in NO2 emissions: as policies became stricter, emissions significantly decreased in most countries. In contrast, in countries with lower population densities, stricter policies paradoxically led to an increase in emissions. These findings underscore the importance of considering demographic factors and geographical context in the formulation and implementation of environmental policies.</p

An Evaluation System for Agriculture and Tourism Coupling Degree of Rural Complex Based on Production-living-ecological Space

Rural complex is a bright spot for China to realize the development of new industries for rural revitalization. Starting from the concept, development history and current situation of rural complex, based on the perspective of production-living-ecological space, according to the different characteristics of agriculture and tourism, 3 secondary indices and 18 tertiary indices were selected, and each of them was weighted using the Delphi expert consultation method and analytic hierarchy process (AHP) to construct an evaluation system for the coupling degree of agriculture and tourism in rural complex. The results show gross domestic product and forest coverage have the most significant impact on agriculture, economic benefits and the ecological water quality of tourist attractions have the most significant impact on the tourism industry, and tourism economic benefits and agricultural ecological benefits have the most significant impact on rural complex

Physiological effect of graphene oxide on tobacco BY-2 suspension cells and its immigration

More and more attentions are paid to the potential effect of graphene oxide (GO) in environment and human beings. In order to evaluate the effect of GO on plant, tobacco BY-2 suspension cells were employed as material, and the physiological effect of GO on tobacco BY-2 suspension cells and its immigration were investigated. The results showed that low concentrations of GO (25 and 50 μg/mL) promoted cells growth (increased by 11.22 % in 50 μg/mL GO), while higher concentrations of GO (100 and 200 μg/mL) induced inhibition in cell growth (decreased by 9.68 % in 200 μg/mL GO). GO caused an increment in activity levels of SOD, POD and CAT, but the activity levels decreased with the extension of culture time in higher concentration. The results showed that GO could make cell nuclei fragment and loose in a higher concentration. These results imply that there is an adverse effect of GO on plant cells, and suggest that nano pollution should be paid attention to

Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising

Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://github.com/nagejacob/SpatiallyAdaptiveSSID.Comment: CVPR 2023 Camera Read