68 research outputs found
Attribute-Guided Face Generation Using Conditional CycleGAN
We are interested in attribute-guided face generation: given a low-res face
input image, an attribute vector that can be extracted from a high-res image
(attribute image), our new method generates a high-res face image for the
low-res input that satisfies the given attributes. To address this problem, we
condition the CycleGAN and propose conditional CycleGAN, which is designed to
1) handle unpaired training data because the training low/high-res and high-res
attribute images may not necessarily align with each other, and to 2) allow
easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN,
which can synthesize realistic face images with appearance easily controlled by
user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
the attribute image as identity to produce the corresponding conditional vector
and by incorporating a face verification network, the attribute-guided network
becomes the identity-guided conditional CycleGAN which produces impressive and
interesting results on identity transfer. We demonstrate three applications on
identity-guided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method.Comment: ECCV 201
Investigate the interaction between dark matter and dark energy
In this paper we investigate the interaction between dark matter and dark
energy by considering two different interacting scenarios, i.e. the cases of
constant interaction function and variable interaction function. By fitting the
current observational data to constrain the interacting models, it is found
that the interacting strength is non-vanishing, but weak for the case of
constant interaction function, and the interaction is not obvious for the case
of variable interaction function. In addition, for seeing the influence from
interaction we also investigate the evolutions of interaction function,
effective state parameter for dark energy and energy density of dark matter. At
last some geometrical quantities in the interacting scenarios are discussed.Comment: 14 pages, 6 figure
Influence of Waterside Buildings’ Layout on Wind Environment and the Relation with Design Based on a Case Study of the She Kou Residential District
It is important to improve residential thermal comfort in the high dense cities, in which wind environment is crucial. Waterside buildings take an advantage of micro-hydrological-climate in summer that should be used to enhance residential thermal comfort especially in the subtropical region. In order to propose design approaches according to the outdoor thermal comfort of the waterside residential, a case study of Shenzhen She Kou residential district has been made. It focused on various factors that could have influence on wind environment for improving thermal comfort. Using wind velocity ratio (ΔRi) criterion, factors of building development volume, building direction and layout pattern, open space arrangement etc. have been broadly explored using FLUENT simulation. To planning parameters, the Floor Area Ratio (FAR) is significantly influence wind environment, the smaller FAR is better. To the vertical layout of the buildings, multi-storey layout and multi-storey & sub high-rise mixed layout would provide better wind environment. To the horizontal layout, the determinant is better than the peripheral. Other factors such as the buildings’ direction towards the road, buildings’ height, and open space setting, have influence on wind environment yet. In general, the more benefit of design layout for wind breezing, the better wind environment it could ge
Abdominal multi-organ segmentation in CT using Swinunter
Abdominal multi-organ segmentation in computed tomography (CT) is crucial for
many clinical applications including disease detection and treatment planning.
Deep learning methods have shown unprecedented performance in this perspective.
However, it is still quite challenging to accurately segment different organs
utilizing a single network due to the vague boundaries of organs, the complex
background, and the substantially different organ size scales. In this work we
used make transformer-based model for training. It was found through previous
years' competitions that basically all of the top 5 methods used CNN-based
methods, which is likely due to the lack of data volume that prevents
transformer-based methods from taking full advantage. The thousands of samples
in this competition may enable the transformer-based model to have more
excellent results. The results on the public validation set also show that the
transformer-based model can achieve an acceptable result and inference time.Comment: 8pages. arXiv admin note: text overlap with arXiv:2201.01266 by other
author
Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation
This paper seeks to address the dense labeling problems where a significant
fraction of the dataset can be pruned without sacrificing much accuracy. We
observe that, on standard medical image segmentation benchmarks, the loss
gradient norm-based metrics of individual training examples applied in image
classification fail to identify the important samples. To address this issue,
we propose a data pruning method by taking into consideration the training
dynamics on target regions using Dynamic Average Dice (DAD) score. To the best
of our knowledge, we are among the first to address the data importance in
dense labeling tasks in the field of medical image analysis, making the
following contributions: (1) investigating the underlying causes with rigorous
empirical analysis, and (2) determining effective data pruning approach in
dense labeling problems. Our solution can be used as a strong yet simple
baseline to select important examples for medical image segmentation with
combined data sources.Comment: Accepted by ICML workshops 202
Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation
We aim at incorporating explicit shape information into current 3D organ
segmentation models. Different from previous works, we formulate shape learning
as an in-painting task, which is named Masked Label Mask Modeling (MLM).
Through MLM, learnable mask tokens are fed into transformer blocks to complete
the label mask of organ. To transfer MLM shape knowledge to target, we further
propose a novel shape-aware self-distillation with both in-painting
reconstruction loss and pseudo loss. Extensive experiments on five public organ
segmentation datasets show consistent improvements over prior arts with at
least 1.2 points gain in the Dice score, demonstrating the effectiveness of our
method in challenging unsupervised domain adaptation scenarios including: (1)
In-domain organ segmentation; (2) Unseen domain segmentation and (3) Unseen
organ segmentation. We hope this work will advance shape analysis and geometric
learning in medical imaging
Label-Assemble: Leveraging Multiple Datasets with Partial Labels
The success of deep learning relies heavily on large and diverse datasets
with extensive labels, but we often only have access to several small datasets
associated with partial labels. In this paper, we start a new initiative,
"Label-Assemble", that aims to unleash the full potential of partially labeled
data from an assembly of public datasets. Specifically, we introduce a new
dynamic adapter to encode different visual tasks, which addresses the
challenges of incomparable, heterogeneous, or even conflicting labeling
protocols. We also employ pseudo-labeling and consistency constraints to
harness data with missing labels and to mitigate the domain gap across
datasets. From rigorous evaluations on three natural imaging and six medical
imaging tasks, we discover that learning from "negative examples" facilitates
both classification and segmentation of classes of interest. This sheds new
light on the computer-aided diagnosis of rare diseases and emerging pandemics,
wherein "positive examples" are hard to collect, yet "negative examples" are
relatively easier to assemble. Apart from exceeding prior arts in the ChestXray
benchmark, our model is particularly strong in identifying diseases of minority
classes, yielding over 3-point improvement on average. Remarkably, when using
existing partial labels, our model performance is on-par with that using full
labels, eliminating the need for an additional 40% of annotation costs. Code
will be made available at https://github.com/MrGiovanni/LabelAssemble
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