4,422 research outputs found
Densely Connected Pyramid Dehazing Network
We propose a new end-to-end single image dehazing method, called Densely
Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the
transmission map, atmospheric light and dehazing all together. The end-to-end
learning is achieved by directly embedding the atmospheric scattering model
into the network, thereby ensuring that the proposed method strictly follows
the physics-driven scattering model for dehazing. Inspired by the dense network
that can maximize the information flow along features from different levels, we
propose a new edge-preserving densely connected encoder-decoder structure with
multi-level pyramid pooling module for estimating the transmission map. This
network is optimized using a newly introduced edge-preserving loss function. To
further incorporate the mutual structural information between the estimated
transmission map and the dehazed result, we propose a joint-discriminator based
on generative adversarial network framework to decide whether the corresponding
dehazed image and the estimated transmission map are real or fake. An ablation
study is conducted to demonstrate the effectiveness of each module evaluated at
both estimated transmission map and dehazed result. Extensive experiments
demonstrate that the proposed method achieves significant improvements over the
state-of-the-art methods. Code will be made available at:
https://github.com/hezhangsprinte
Auto-Detection of Safety Issues in Baby Products
Every year, thousands of people receive consumer product related injuries.
Research indicates that online customer reviews can be processed to
autonomously identify product safety issues. Early identification of safety
issues can lead to earlier recalls, and thus fewer injuries and deaths. A
dataset of product reviews from Amazon.com was compiled, along with
\emph{SaferProducts.gov} complaints and recall descriptions from the Consumer
Product Safety Commission (CPSC) and European Commission Rapid Alert system. A
system was built to clean the collected text and to extract relevant features.
Dimensionality reduction was performed by computing feature relevance through a
Random Forest and discarding features with low information gain. Various
classifiers were analyzed, including Logistic Regression, SVMs,
Na{\"i}ve-Bayes, Random Forests, and an Ensemble classifier. Experimentation
with various features and classifier combinations resulted in a logistic
regression model with 66\% precision in the top 50 reviews surfaced. This
classifier outperforms all benchmarks set by related literature and consumer
product safety professionals.Comment: To appear in proceedings of The 31st IEA-AIE 2018, June 25-28, 2018,
Montreal, Canad
The Visual Centrifuge: Model-Free Layered Video Representations
True video understanding requires making sense of non-lambertian scenes where
the color of light arriving at the camera sensor encodes information about not
just the last object it collided with, but about multiple mediums -- colored
windows, dirty mirrors, smoke or rain. Layered video representations have the
potential of accurately modelling realistic scenes but have so far required
stringent assumptions on motion, lighting and shape. Here we propose a
learning-based approach for multi-layered video representation: we introduce
novel uncertainty-capturing 3D convolutional architectures and train them to
separate blended videos. We show that these models then generalize to single
videos, where they exhibit interesting abilities: color constancy, factoring
out shadows and separating reflections. We present quantitative and qualitative
results on real world videos.Comment: Appears in: 2019 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2019). This arXiv contains the CVPR Camera Ready version of
the paper (although we have included larger figures) as well as an appendix
detailing the model architectur
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates
Many applications of computational social science aim to infer causal
conclusions from non-experimental data. Such observational data often contains
confounders, variables that influence both potential causes and potential
effects. Unmeasured or latent confounders can bias causal estimates, and this
has motivated interest in measuring potential confounders from observed text.
For example, an individual's entire history of social media posts or the
content of a news article could provide a rich measurement of multiple
confounders. Yet, methods and applications for this problem are scattered
across different communities and evaluation practices are inconsistent. This
review is the first to gather and categorize these examples and provide a guide
to data-processing and evaluation decisions. Despite increased attention on
adjusting for confounding using text, there are still many open problems, which
we highlight in this paper.Comment: Accepted to ACL 202
Fair Inference On Outcomes
In this paper, we consider the problem of fair statistical inference
involving outcome variables. Examples include classification and regression
problems, and estimating treatment effects in randomized trials or
observational data. The issue of fairness arises in such problems where some
covariates or treatments are "sensitive," in the sense of having potential of
creating discrimination. In this paper, we argue that the presence of
discrimination can be formalized in a sensible way as the presence of an effect
of a sensitive covariate on the outcome along certain causal pathways, a view
which generalizes (Pearl, 2009). A fair outcome model can then be learned by
solving a constrained optimization problem. We discuss a number of
complications that arise in classical statistical inference due to this view
and provide workarounds based on recent work in causal and semi-parametric
inference
Non-aligned supervision for Real Image Dehazing
Removing haze from real-world images is challenging due to unpredictable
weather conditions, resulting in misaligned hazy and clear image pairs. In this
paper, we propose a non-aligned supervision framework that consists of three
networks - dehazing, airlight, and transmission. In particular, we explore a
non-alignment setting by utilizing a clear reference image that is not aligned
with the hazy input image to supervise the dehazing network through a
multi-scale reference loss that compares the features of the two images. Our
setting makes it easier to collect hazy/clear image pairs in real-world
environments, even under conditions of misalignment and shift views. To
demonstrate this, we have created a new hazy dataset called "Phone-Hazy", which
was captured using mobile phones in both rural and urban areas. Additionally,
we present a mean and variance self-attention network to model the infinite
airlight using dark channel prior as position guidance, and employ a channel
attention network to estimate the three-channel transmission. Experimental
results show that our framework outperforms current state-of-the-art methods in
the real-world image dehazing. Phone-Hazy and code will be available at
https://github.com/hello2377/NSDNet
Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation
Satellite images are often contaminated by clouds. Cloud removal has received
much attention due to the wide range of satellite image applications. As the
clouds thicken, the process of removing the clouds becomes more challenging. In
such cases, using auxiliary images such as near-infrared or synthetic aperture
radar (SAR) for reconstructing is common. In this study, we attempt to solve
the problem using two generative adversarial networks (GANs). The first
translates SAR images into optical images, and the second removes clouds using
the translated images of prior GAN. Also, we propose dilated residual inception
blocks (DRIBs) instead of vanilla U-net in the generator networks and use
structural similarity index measure (SSIM) in addition to the L1 Loss function.
Reducing the number of downsamplings and expanding receptive fields by dilated
convolutions increase the quality of output images. We used the SEN1-2 dataset
to train and test both GANs, and we made cloudy images by adding synthetic
clouds to optical images. The restored images are evaluated with PSNR and SSIM.
We compare the proposed method with state-of-the-art deep learning models and
achieve more accurate results in both SAR-to-optical translation and cloud
removal parts
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow
We propose an end-to-end trained neural networkarchitecture to robustly
predict the complex dynamics of fluid flows with high temporal stability. We
focus on single-phase smoke simulations in 2D and 3D based on the
incompressible Navier-Stokes (NS) equations, which are relevant for a wide
range of practical problems. To achieve stable predictions for long-term flow
sequences, a convolutional neural network (CNN) is trained for spatial
compression in combination with a temporal prediction network that consists of
stacked Long Short-Term Memory (LSTM) layers. Our core contribution is a novel
latent space subdivision (LSS) to separate the respective input quantities into
individual parts of the encoded latent space domain. This allows to
distinctively alter the encoded quantities without interfering with the
remaining latent space values and hence maximizes external control. By
selectively overwriting parts of the predicted latent space points, our
proposed method is capable to robustly predict long-term sequences of complex
physics problems. In addition, we highlight the benefits of a recurrent
training on the latent space creation, which is performed by the spatial
compression network.Comment: https://ge.in.tum.de/publications/latent-space-subdivision
The Return of the Beehives, Brylcreem and Botanical! An Historical Review of Hair Care Practices with a view to Opportunities for Sustainable Design
This paper considers hair care as a network of activities and routines which have consequences for environmental sustainability and which may be modified by design. It proposes that together with cultural knowledge, embodied skill and objects, these activities can be thought of as ‘practices’ that are reproduced and also change through time (Shove 2006). They consume resources and are therefore implicated in the issue of environmental sustainability.
The paper draws on research into hair care practices conducted through in-depth interviews with female participants, as part of the first author’s PhD study. The discussion here however centres on historical work and Shove’s (2003) writing on bathing to explore the changing products and substances including ideas, technological and infrastructural aspects of cleansing and conditioning hair. Because these factors may determine when to wash or not to wash your hair they affect resource consumption.
The paper concludes by outlining opportunities for sustainable design that follow from the insights gained by investigating the history of hair care in relation to the data collected during in-depth interviews. It highlights, as outlined by Hand et al (2005), that the resources consumed through hair care are influenced by the integrative nature of hair care as a practice rather than by individuals being dedicated to sustainability.
To concentrate on single products without taking into account that hair care is practiced in everyday life is not likely to provide opportunities for sustainable living. What is considered ‘normal’ standards of hair care and means to achieve them needs to be conceptualised to identify opportunities to modify what is considered ‘normal’ through design.
Keywords:
Everyday Practices, Sustainable Design</p
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