3,088 research outputs found
Towards Real-time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-decoder Architecture
In winter scenes, the degradation of images taken under snow can be pretty
complex, where the spatial distribution of snowy degradation is varied from
image to image. Recent methods adopt deep neural networks to directly recover
clean scenes from snowy images. However, due to the paradox caused by the
variation of complex snowy degradation, achieving reliable High-Definition
image desnowing performance in real time is a considerable challenge. We
develop a novel Efficient Pyramid Network with asymmetrical encoder-decoder
architecture for real-time HD image desnowing. The general idea of our proposed
network is to utilize the multi-scale feature flow fully and implicitly mine
clean cues from features. Compared with previous state-of-the-art desnowing
methods, our approach achieves a better complexity-performance trade-off and
effectively handles the processing difficulties of HD and Ultra-HD images.
The extensive experiments on three large-scale image desnowing datasets
demonstrate that our method surpasses all state-of-the-art approaches by a
large margin both quantitatively and qualitatively, boosting the PSNR metric
from 31.76 dB to 34.10 dB on the CSD test dataset and from 28.29 dB to 30.87 dB
on the SRRS test dataset
MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing
Image restoration of snow scenes in severe weather is a difficult task. Snow
images have complex degradations and are cluttered over clean images, changing
the distribution of clean images. The previous methods based on CNNs are
challenging to remove perfectly in restoring snow scenes due to their local
inductive biases' lack of a specific global modeling ability. In this paper, we
apply the vision transformer to the task of snow removal from a single image.
Specifically, we propose a parallel network architecture split along the
channel, performing local feature refinement and global information modeling
separately. We utilize a channel shuffle operation to combine their respective
strengths to enhance network performance. Second, we propose the MSP module,
which utilizes multi-scale avgpool to aggregate information of different sizes
and simultaneously performs multi-scale projection self-attention on multi-head
self-attention to improve the representation ability of the model under
different scale degradations. Finally, we design a lightweight and simple local
capture module, which can refine the local capture capability of the model.
In the experimental part, we conduct extensive experiments to demonstrate the
superiority of our method. We compared the previous snow removal methods on
three snow scene datasets. The experimental results show that our method
surpasses the state-of-the-art methods with fewer parameters and computation.
We achieve substantial growth by 1.99dB and SSIM 0.03 on the CSD test dataset.
On the SRRS and Snow100K datasets, we also increased PSNR by 2.47dB and 1.62dB
compared with the Transweather approach and improved by 0.03 in SSIM. In the
visual comparison section, our MSP-Former also achieves better visual effects
than existing methods, proving the usability of our method
Development of Social Life Circumstance of Urban Fringe Settlements in China Central Region
Along with the triumphant advance of urbanization in central China central region and disorderly unwinding of cities, urban fringe settlements are incorporated into urban expansion territory, so that villagers became landless peasants. This paper is based on the comparison and analysis on the social life circumstance of traditional settlements and modern urban fringe settlements, thereby exploring the causes and rules of development of social life circumstance of urban fringe settlements in the urbanization process
Mechanical cooling at the bistable regime of a dissipative optomechanical cavity with a Kerr medium
In this paper, we study static bistability and mechanical cooling of a
dissipative optomechanical cavity filled with a Kerr medium. The system
exhibits optical bistability for a wide input-power range with the power
threshold being greatly reduced, in contrast to the case of purely dissipative
coupling. At the bistable regime, the membrane can be effectively cooled down
to a few millikelvin from the room temperature under the unresolved sideband
condition, where the effective mechanical temperature is a nonmonotonic
function of intracavity intensity and reaches its minimum near the turning
point of the upper stable branch. When the system is in the cryogenics
environment, the effective mechanical temperature at the bistable regime shows
a similar feature as in the room temperature case, but the optimal cooling
appears at the monostable regime and approaches the mechanical ground state.
Our results are of interest for further understanding bistable optomechanical
systems, which have many applications in nonclassical state preparations and
quantum information processing.Comment: 10 pages, 5 figure
Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term
Deep Neural Networks (DNNs) generalization is known to be closely related to
the flatness of minima, leading to the development of Sharpness-Aware
Minimization (SAM) for seeking flatter minima and better generalization. In
this paper, we revisit the loss of SAM and propose a more general method,
called WSAM, by incorporating sharpness as a regularization term. We prove its
generalization bound through the combination of PAC and Bayes-PAC techniques,
and evaluate its performance on various public datasets. The results
demonstrate that WSAM achieves improved generalization, or is at least highly
competitive, compared to the vanilla optimizer, SAM and its variants. The code
is available at
https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers.Comment: 10 pages. Accepted as a conference paper at KDD '2
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