159 research outputs found
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
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
Customizing the promotion strategies of integrated air-bus service based on passenger satisfaction
The integrated air-bus service expands the catchment area and alleviates congestion of regional airports. To gain further insights into the unexplored potential attributes of the integrated service that generate passenger satisfaction, this paper utilizes a two-stage analysis approach to identify the key promotion factors for passengers from different constituents. Based on the survey data collected in Nanjing Lukou International Airport, this paper 1) uses k-means clustering to categorize respondents into four groups. 2) Combines the gradient boosting decision tree and impact asymmetry analysis to identify the attributes that have nonlinear influences on the overall service satisfaction for each group respectively. Results suggest that the timetable of the airport bus is critical for all passenger groups. Interestingly, there are noticeable differences in passenger satisfaction with the accessibility, cost affordability, comfort, reliability, and integration of the integrated service, providing the basis for customizing service promotion strategies among different passenger groups and airports.</p
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
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