178 research outputs found
3D-aware Image Generation using 2D Diffusion Models
In this paper, we introduce a novel 3D-aware image generation method that
leverages 2D diffusion models. We formulate the 3D-aware image generation task
as multiview 2D image set generation, and further to a sequential
unconditional-conditional multiview image generation process. This allows us to
utilize 2D diffusion models to boost the generative modeling power of the
method. Additionally, we incorporate depth information from monocular depth
estimators to construct the training data for the conditional diffusion model
using only still images. We train our method on a large-scale dataset, i.e.,
ImageNet, which is not addressed by previous methods. It produces high-quality
images that significantly outperform prior methods. Furthermore, our approach
showcases its capability to generate instances with large view angles, even
though the training images are diverse and unaligned, gathered from
"in-the-wild" real-world environments.Comment: Website: https://jeffreyxiang.github.io/ivid
Automatic single fish detection with a commercial echosounder using YOLO v5 and its application for echosounder calibration
Nowadays, most fishing vessels are equipped with high-resolution commercial echo sounders. However, many instruments cannot be calibrated and missing data occur frequently. These problems impede the collection of acoustic data by commercial fishing vessels, which are necessary for species classification and stock assessment. In this study, an automatic detection and classification model for echo traces of the Pacific saury (Cololabis saira) was trained based on the algorithm YOLO v5m. The in situ measurement value of the Pacific saury was measured using single fish echo trace. Rapid calibration of the commercial echo sounder was achieved based on the living fish calibration method. According to the results, the maximum precision, recall, and average precision values of the trained model were 0.79, 0.68, and 0.71, respectively. The maximum F1 score of the model was 0.66 at a confidence level of 0.454. The living fish calibration offset values obtained at two sites in the field were 116.30 dB and 118.19 dB. The sphere calibration offset value obtained in the laboratory using the standard sphere method was 117.65 dB. The differences between in situ and laboratory calibrations were 1.35 dB and 0.54 dB, both of which were within the normal range
Hierarchical-level rain image generative model based on GAN
Autonomous vehicles are exposed to various weather during operation, which is
likely to trigger the performance limitations of the perception system, leading
to the safety of the intended functionality (SOTIF) problems. To efficiently
generate data for testing the performance of visual perception algorithms under
various weather conditions, a hierarchical-level rain image generative model,
rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on
the generative adversarial network (GAN) and can generate images of light,
medium, and heavy rain. Different rain intensities are introduced as labels in
conditional GAN (CGAN). Meanwhile, the model structure is optimized and the
training strategy is adjusted to alleviate the problem of mode collapse. In
addition, natural rain images of different intensities are collected and
processed for model training and validation. Compared with the two baseline
models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of
RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the
structural similarity (SSIM) is improved by 18% and 8%, respectively. The
ablation experiments are also carried out to validate the effectiveness of the
model tuning
AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections
Previous animatable 3D-aware GANs for human generation have primarily focused
on either the human head or full body. However, head-only videos are relatively
uncommon in real life, and full body generation typically does not deal with
facial expression control and still has challenges in generating high-quality
results. Towards applicable video avatars, we present an animatable 3D-aware
GAN that generates portrait images with controllable facial expression, head
pose, and shoulder movements. It is a generative model trained on unstructured
2D image collections without using 3D or video data. For the new task, we base
our method on the generative radiance manifold representation and equip it with
learnable facial and head-shoulder deformations. A dual-camera rendering and
adversarial learning scheme is proposed to improve the quality of the generated
faces, which is critical for portrait images. A pose deformation processing
network is developed to generate plausible deformations for challenging regions
such as long hair. Experiments show that our method, trained on unstructured 2D
images, can generate diverse and high-quality 3D portraits with desired control
over different properties.Comment: SIGGRAPH Asia 2023. Project Page:
https://yuewuhkust.github.io/AniPortraitGAN
Visual impairment and spectacle coverage rate in Baoshan district, China: population-based study
BACKGROUND: To investigate the prevalence and risk factors of visual impairment associated with refractive error and the unmet need for spectacles in a special suburban senior population in Baoshan District of Shanghai, one of several rural areas undergoing a transition from rural to urban area, where data of visual impairment are limited. METHODS: The study was a population based survey of 4545 Chinese aged (age: >60Â years or older ) at Baoshan, Shanghai, in 2009. One copy of questionnaire was completed for each subject. Examinations included a standardized refraction and measurement of presenting and best corrected visual acuity (BCVA) as well as tonometry, slit lamp biomicroscopy, and fundus photography. RESULTS: The prevalence of mild (6/12 to 6/18), moderate (6/18 to 6/60) and severe visual impairment was 12.59%, 8.38% and 0.44%, respectively, and 5.26%, 3.06% and 0.09% with refractive correction. Visual impairment was associated with age, gender, education and career, but not insurance . The prevalence of correctable visual impairment was 5.81% (using 6/18 cutoff) and 13.18% (using 6/12 cutoff). Senior people and women were significantly at a higher risk of correctable visual impairment, while the well-educated on the contrary. The prevalence of undercorrected refractive error (improves by 2 or more lines with refraction) was 24.84%, and the proportion with undercorrected refractive error for mild, moderate , severe and no visual impairment was 61.54%, 67.98%, 60.00% and 14.10%, respectively. The spectacle coverage rate was 44.12%. Greater unmet need for spectacles was observed among elderly people, females, non-peasant, and subjects with less education and astigmatism only. CONCLUSIONS: High prevalence of visual impairment, visual impairment alleviated by refractive correction, and low spectacle coverage existed among the senior population in Baoshan District of Shanghai. Education for the public of the importance of regular examination and appropriate and accessible refraction service might be helpful to solve the problem
The REST Gene Signature Predicts Drug Sensitivity in Neuroblastoma Cell Lines and Is Significantly Associated with Neuroblastoma Tumor Stage
Neuroblastoma is the most common and deadly solid tumor in children, and there is currently no effective treatment available for neuroblastoma patients. The repressor element-1 silencing transcription (REST) factor has been found to play important roles in the regulation of neural differentiation and tumorigenesis. Recently, a REST signature consisting of downstream targets of REST has been reported to have clinical relevance in both breast cancer and glioblastoma. However it remains unclear how the REST signature works in neuroblastoma. Publicly available datasets were mined and bioinformatic approaches were used to investigate the utility of the REST signature in neuroblastoma with both preclinical and real patient data. The REST signature was found to be associated with drug sensitivity in neuroblastoma cell lines. Further, neuroblastoma patients with enhanced REST activity are significantly associated with higher clinical stages. Loss of heterozygosity on chromosome 11q23, which occurs in a large subset of high-risk neuroblastomas, tends to be correlated with high REST activity, with marginal significance. In conclusion, the REST signature has important implications for targeted therapy, and it is a prognostic factor in neuroblastoma patients
Order flow volatility and equity costs of capital
Ministry of Education, Singapore under its Academic Research Funding Tier 1; Sim Kee Boon Institute for Financial Economics at Singapore Management Universit
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