8 research outputs found
Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity
Image deraining is a typical low-level image restoration task, which aims at
decomposing the rainy image into two distinguishable layers: the clean image
layer and the rain layer. Most of the existing learning-based deraining methods
are supervisedly trained on synthetic rainy-clean pairs. The domain gap between
the synthetic and real rains makes them less generalized to different real
rainy scenes. Moreover, the existing methods mainly utilize the property of the
two layers independently, while few of them have considered the mutually
exclusive relationship between the two layers. In this work, we propose a novel
non-local contrastive learning (NLCL) method for unsupervised image deraining.
Consequently, we not only utilize the intrinsic self-similarity property within
samples but also the mutually exclusive property between the two layers, so as
to better differ the rain layer from the clean image. Specifically, the
non-local self-similarity image layer patches as the positives are pulled
together and similar rain layer patches as the negatives are pushed away. Thus
the similar positive/negative samples that are close in the original space
benefit us to enrich more discriminative representation. Apart from the
self-similarity sampling strategy, we analyze how to choose an appropriate
feature encoder in NLCL. Extensive experiments on different real rainy datasets
demonstrate that the proposed method obtains state-of-the-art performance in
real deraining.Comment: 10 pages, 10 figures, accept to 2022CVP
PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models
Recent studies showed that Photoplethysmography (PPG) sensors embedded in
wearable devices can estimate heart rate (HR) with high accuracy. However,
despite of prior research efforts, applying PPG sensor based HR estimation to
embedded devices still faces challenges due to the energy-intensive
high-frequency PPG sampling and the resource-intensive machine-learning models.
In this work, we aim to explore HR estimation techniques that are more suitable
for lower-power and resource-constrained embedded devices. More specifically,
we seek to design techniques that could provide high-accuracy HR estimation
with low-frequency PPG sampling, small model size, and fast inference time.
First, we show that by combining signal processing and ML, it is possible to
reduce the PPG sampling frequency from 125 Hz to only 25 Hz while providing
higher HR estimation accuracy. This combination also helps to reduce the ML
model feature size, leading to smaller models. Additionally, we present a
comprehensive analysis on different ML models and feature sizes to compare
their accuracy, model size, and inference time. The models explored include
Decision Tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Support
vector machines (SVM), and Multi-layer perceptron (MLP). Experiments were
conducted using both a widely-utilized dataset and our self-collected dataset.
The experimental results show that our method by combining signal processing
and ML had only 5% error for HR estimation using low-frequency PPG data.
Moreover, our analysis showed that DT models with 10 to 20 input features
usually have good accuracy, while are several magnitude smaller in model sizes
and faster in inference time
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
Most of the existing learning-based deraining methods are supervisedly
trained on synthetic rainy-clean pairs. The domain gap between the synthetic
and real rain makes them less generalized to complex real rainy scenes.
Moreover, the existing methods mainly utilize the property of the image or rain
layers independently, while few of them have considered their mutually
exclusive relationship. To solve above dilemma, we explore the intrinsic
intra-similarity within each layer and inter-exclusiveness between two layers
and propose an unsupervised non-local contrastive learning (NLCL) deraining
method. The non-local self-similarity image patches as the positives are
tightly pulled together, rain patches as the negatives are remarkably pushed
away, and vice versa. On one hand, the intrinsic self-similarity knowledge
within positive/negative samples of each layer benefits us to discover more
compact representation; on the other hand, the mutually exclusive property
between the two layers enriches the discriminative decomposition. Thus, the
internal self-similarity within each layer (similarity) and the external
exclusive relationship of the two layers (dissimilarity) serving as a generic
image prior jointly facilitate us to unsupervisedly differentiate the rain from
clean image. We further discover that the intrinsic dimension of the non-local
image patches is generally higher than that of the rain patches. This motivates
us to design an asymmetric contrastive loss to precisely model the compactness
discrepancy of the two layers for better discriminative decomposition. In
addition, considering that the existing real rain datasets are of low quality,
either small scale or downloaded from the internet, we collect a real
large-scale dataset under various rainy kinds of weather that contains
high-resolution rainy images.Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with
arXiv:2203.1150
Warming-driven migration of core microbiota indicates soil property changes at continental scale
Terrestrial species are predicted to migrate northward under global warming conditions, yet little is known about the direction and magnitude of change in microbial distribution patterns. In this continental-scale study with more than 1600 forest soil samples, we verify the existence of core microbiota and lump them into a manageable number of eco-clusters based on microbial habitat preferences. By projecting the abundance differences of eco-clusters between future and current climatic conditions, we observed the potential warming-driven migration of the core microbiota under warming, partially verified by a field warming experiment at Southwest China. Specifically, the species that favor low pH are potentially expanding and moving northward to medium-latitudes (25 degrees-45 degrees N), potentially implying that warm temperate forest would be under threat of soil acidification with warming. The eco-cluster of high-pH with high-annual mean temperature (AMT) experienced significant abundance increases at middle- (35 degrees-45 degrees N) to high-latitudes (> 45 degrees N), especially under Representative Concentration Pathway (RCP) 8.5, likely resulting in northward expansion. Furthermore, the eco-cluster that favors low-soil organic carbon (SOC) was projected to increase under warming scenarios at low-latitudes ( 45 degrees N) the changes in relative abundance of this eco-cluster is inversely related with the temperature variation trends, suggesting microbes-mediated soil organic carbon changes are more responsive to temperature variation in colder areas. These results have vital implications for the migration direction of microbial communities and its potential ecological consequences in future warming scenarios. (C) 2021 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved