82 research outputs found
Joint Correcting and Refinement for Balanced Low-Light Image Enhancement
Low-light image enhancement tasks demand an appropriate balance among
brightness, color, and illumination. While existing methods often focus on one
aspect of the image without considering how to pay attention to this balance,
which will cause problems of color distortion and overexposure etc. This
seriously affects both human visual perception and the performance of
high-level visual models. In this work, a novel synergistic structure is
proposed which can balance brightness, color, and illumination more
effectively. Specifically, the proposed method, so-called Joint Correcting and
Refinement Network (JCRNet), which mainly consists of three stages to balance
brightness, color, and illumination of enhancement. Stage 1: we utilize a basic
encoder-decoder and local supervision mechanism to extract local information
and more comprehensive details for enhancement. Stage 2: cross-stage feature
transmission and spatial feature transformation further facilitate color
correction and feature refinement. Stage 3: we employ a dynamic illumination
adjustment approach to embed residuals between predicted and ground truth
images into the model, adaptively adjusting illumination balance. Extensive
experiments demonstrate that the proposed method exhibits comprehensive
performance advantages over 21 state-of-the-art methods on 9 benchmark
datasets. Furthermore, a more persuasive experiment has been conducted to
validate our approach the effectiveness in downstream visual tasks (e.g.,
saliency detection). Compared to several enhancement models, the proposed
method effectively improves the segmentation results and quantitative metrics
of saliency detection. The source code will be available at
https://github.com/woshiyll/JCRNet
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
With a focus on abnormal events contained within untrimmed videos, there is
increasing interest among researchers in video anomaly detection. Among
different video anomaly detection scenarios, weakly-supervised video anomaly
detection poses a significant challenge as it lacks frame-wise labels during
the training stage, only relying on video-level labels as coarse supervision.
Previous methods have made attempts to either learn discriminative features in
an end-to-end manner or employ a twostage self-training strategy to generate
snippet-level pseudo labels. However, both approaches have certain limitations.
The former tends to overlook informative features at the snippet level, while
the latter can be susceptible to noises. In this paper, we propose an Anomalous
Attention mechanism for weakly-supervised anomaly detection to tackle the
aforementioned problems. Our approach takes into account snippet-level encoded
features without the supervision of pseudo labels. Specifically, our approach
first generates snippet-level anomalous attention and then feeds it together
with original anomaly scores into a Multi-branch Supervision Module. The module
learns different areas of the video, including areas that are challenging to
detect, and also assists the attention optimization. Experiments on benchmark
datasets XDViolence and UCF-Crime verify the effectiveness of our method.
Besides, thanks to the proposed snippet-level attention, we obtain a more
precise anomaly localization
Prototype-guided Cross-task Knowledge Distillation for Large-scale Models
Recently, large-scale pre-trained models have shown their advantages in many
tasks. However, due to the huge computational complexity and storage
requirements, it is challenging to apply the large-scale model to real scenes.
A common solution is knowledge distillation which regards the large-scale model
as a teacher model and helps to train a small student model to obtain a
competitive performance. Cross-task Knowledge distillation expands the
application scenarios of the large-scale pre-trained model. Existing knowledge
distillation works focus on directly mimicking the final prediction or the
intermediate layers of the teacher model, which represent the global-level
characteristics and are task-specific. To alleviate the constraint of different
label spaces, capturing invariant intrinsic local object characteristics (such
as the shape characteristics of the leg and tail of the cattle and horse) plays
a key role. Considering the complexity and variability of real scene tasks, we
propose a Prototype-guided Cross-task Knowledge Distillation (ProC-KD) approach
to transfer the intrinsic local-level object knowledge of a large-scale teacher
network to various task scenarios. First, to better transfer the generalized
knowledge in the teacher model in cross-task scenarios, we propose a prototype
learning module to learn from the essential feature representation of objects
in the teacher model. Secondly, for diverse downstream tasks, we propose a
task-adaptive feature augmentation module to enhance the features of the
student model with the learned generalization prototype features and guide the
training of the student model to improve its generalization ability. The
experimental results on various visual tasks demonstrate the effectiveness of
our approach for large-scale model cross-task knowledge distillation scenes
Operating Conditions of Hollow Fiber Supported Liquid Membrane for Phenol Extraction from Coal Gasification Wastewater
The extraction and recycling of phenol from high concentration coal gasification wastewater has been studied using polypropylene (PP) hollow fiber membrane and polyvinylidene fluoride (PVDF) hollow fiber membrane as liquid membrane support, the mixture of tributyl phosphate (TBP) and kerosene as liquid membrane phase, and sodium hydroxide as stripping agent in the process of extraction. The experiments investigated the effect of the operating conditions of the hollow fiber supported liquid membrane, such as aqueous phase temperature and the connection forms of membrane modules, on the extraction efficiency of phenol from high concentration coal gasification wastewater. The conclusions obtained from lab scale experiments provided guidance for scale-up experiments. So, in the scale-up experiments, three membrane modules connected in parallel, then three membrane modules connected in series were used to increase the treatment capacity and improve the treatment effect, under the operating conditions of wastewater temperature 20 ËšC, PH 7.5~8.1, flow rate 100 L/h and the concentration of stripping phase 0.1 mol/L, stripping phase flow rate 50 L/h, the extraction efficiency of the PP-TBP supported liquid membrane system was 87.02% and the phenol concentration of effluent was 218.14mg/L. And the phenol concentration of effluent met the requirements of further biodegradation treatment
Fluid computational model for mineral and vegetal pigments diffusing in Chinese color-ink painting
In this paper, simulation of artistic vegetable and mineral pigments diffusing effects of Chinese color-ink painting is presented, using a novel physical model according to the Second Fick's diffusing law and Brownian motion theory. Due to the fact that generation of most art effects depends on complicated pigment-water motion simulation such as diffusing and pigment mixing on and under traditional fabric cotton paper (Xuan paper) surface, the proposed model is found effective for simulating the pigment-water motion in art creating process. Implemented on the GPU, the simulation operations in our system can be accomplished in a real-time manner. The effectiveness of the proposed techniques is validated in our developed Digital Painting System, where various art effects can be successfully re-produced including the Initial area-Darkened initial edge-Diffusion area Lightened diffusion edge (IDDL) effect for vegetable pigments, the Initial area-Darkened initial edge-Diffusion area-Darkened diffusion edge (IDDD) effect for mineral pigments, and multistroke superimposing effects and achieves. In addition, quantitative evaluation is also introduced and shows superior performance of the proposed model in comparison with state-of-the-art techniques
A Canonical Correlation Analysis of AIDS Restriction Genes and Metabolic Pathways Identifies Purine Metabolism as a Key Cooperator
Human immunodeficiency virus causes a severe disease in humans, referred to as immune deficiency syndrome. Studies on the interaction between host genetic factors and the virus have revealed dozens of genes that impact diverse processes in the AIDS disease. To resolve more genetic factors related to AIDS, a canonical correlation analysis was used to determine the correlation between AIDS restriction and metabolic pathway gene expression. The results show that HIV-1 postentry cellular viral cofactors from AIDS restriction genes are coexpressed in human transcriptome microarray datasets. Further, the purine metabolism pathway comprises novel host factors that are coexpressed with AIDS restriction genes. Using a canonical correlation analysis for expression is a reliable approach to exploring the mechanism underlying AIDS
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