32 research outputs found
Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification
Many unsupervised domain adaptive (UDA) person re-identification (ReID)
approaches combine clustering-based pseudo-label prediction with feature
fine-tuning. However, because of domain gap, the pseudo-labels are not always
reliable and there are noisy/incorrect labels. This would mislead the feature
representation learning and deteriorate the performance. In this paper, we
propose to estimate and exploit the credibility of the assigned pseudo-label of
each sample to alleviate the influence of noisy labels, by suppressing the
contribution of noisy samples. We build our baseline framework using the mean
teacher method together with an additional contrastive loss. We have observed
that a sample with a wrong pseudo-label through clustering in general has a
weaker consistency between the output of the mean teacher model and the student
model. Based on this finding, we propose to exploit the uncertainty (measured
by consistency levels) to evaluate the reliability of the pseudo-label of a
sample and incorporate the uncertainty to re-weight its contribution within
various ReID losses, including the identity (ID) classification loss per
sample, the triplet loss, and the contrastive loss. Our uncertainty-guided
optimization brings significant improvement and achieves the state-of-the-art
performance on benchmark datasets.Comment: 9 pages. Accepted to 35th AAAI Conference on Artificial Intelligence
(AAAI 2021
AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort
Story visualization aims to generate a series of images that match the story
described in texts, and it requires the generated images to satisfy high
quality, alignment with the text description, and consistency in character
identities. Given the complexity of story visualization, existing methods
drastically simplify the problem by considering only a few specific characters
and scenarios, or requiring the users to provide per-image control conditions
such as sketches. However, these simplifications render these methods
incompetent for real applications. To this end, we propose an automated story
visualization system that can effectively generate diverse, high-quality, and
consistent sets of story images, with minimal human interactions. Specifically,
we utilize the comprehension and planning capabilities of large language models
for layout planning, and then leverage large-scale text-to-image models to
generate sophisticated story images based on the layout. We empirically find
that sparse control conditions, such as bounding boxes, are suitable for layout
planning, while dense control conditions, e.g., sketches and keypoints, are
suitable for generating high-quality image content. To obtain the best of both
worlds, we devise a dense condition generation module to transform simple
bounding box layouts into sketch or keypoint control conditions for final image
generation, which not only improves the image quality but also allows easy and
intuitive user interactions. In addition, we propose a simple yet effective
method to generate multi-view consistent character images, eliminating the
reliance on human labor to collect or draw character images.Comment: 19 page
Neural Dependencies Emerging from Learning Massive Categories
This work presents two astonishing findings on neural networks learned for
large-scale image classification. 1) Given a well-trained model, the logits
predicted for some category can be directly obtained by linearly combining the
predictions of a few other categories, which we call \textbf{neural
dependency}. 2) Neural dependencies exist not only within a single model, but
even between two independently learned models, regardless of their
architectures. Towards a theoretical analysis of such phenomena, we demonstrate
that identifying neural dependencies is equivalent to solving the Covariance
Lasso (CovLasso) regression problem proposed in this paper. Through
investigating the properties of the problem solution, we confirm that neural
dependency is guaranteed by a redundant logit covariance matrix, which
condition is easily met given massive categories, and that neural dependency is
highly sparse, implying that one category correlates to only a few others. We
further empirically show the potential of neural dependencies in understanding
internal data correlations, generalizing models to unseen categories, and
improving model robustness with a dependency-derived regularizer. Code for this
work will be made publicly available
CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
We present the content deformation field CoDeF as a new type of video
representation, which consists of a canonical content field aggregating the
static contents in the entire video and a temporal deformation field recording
the transformations from the canonical image (i.e., rendered from the canonical
content field) to each individual frame along the time axis.Given a target
video, these two fields are jointly optimized to reconstruct it through a
carefully tailored rendering pipeline.We advisedly introduce some
regularizations into the optimization process, urging the canonical content
field to inherit semantics (e.g., the object shape) from the video.With such a
design, CoDeF naturally supports lifting image algorithms for video processing,
in the sense that one can apply an image algorithm to the canonical image and
effortlessly propagate the outcomes to the entire video with the aid of the
temporal deformation field.We experimentally show that CoDeF is able to lift
image-to-image translation to video-to-video translation and lift keypoint
detection to keypoint tracking without any training.More importantly, thanks to
our lifting strategy that deploys the algorithms on only one image, we achieve
superior cross-frame consistency in processed videos compared to existing
video-to-video translation approaches, and even manage to track non-rigid
objects like water and smog.Project page can be found at
https://qiuyu96.github.io/CoDeF/.Comment: Project Webpage: https://qiuyu96.github.io/CoDeF/, Code:
https://github.com/qiuyu96/CoDe
Streptococcus suis Sequence Type 7 Outbreak, Sichuan, China
An outbreak of Streptococcus suis serotype 2 emerged in the summer of 2005 in Sichuan Province, and sporadic infections occurred in 4 additional provinces of China. In total, 99 S. suis strains were isolated and analyzed in this study: 88 isolates from human patients and 11 from diseased pigs. We defined 98 of 99 isolates as pulse type I by using pulsed-field gel electrophoresis analysis of SmaI-digested chromosomal DNA. Furthermore, multilocus sequence typing classified 97 of 98 members of the pulse type I in the same sequence type (ST), ST-7. Isolates of ST-7 were more toxic to peripheral blood mononuclear cells than ST-1 strains. S. suis ST-7, the causative agent, was a single-locus variant of ST-1 with increased virulence. These findings strongly suggest that ST-7 is an emerging, highly virulent S. suis clone that caused the largest S. suis outbreak ever described
Role of drugs used for chronic disease management on susceptibility and severity of COVID-19: A large case-control study
The study aimed to investigate whether specific medications used in the treatment chronic diseases affected either the development and/ or severity of COVID-19 in a cohort of 610 COVID-19 cases and 48,667 population-based controls from Zheijang, China. Using a cohort of 578 COVID-19 cases and 48,667 population-based controls from Zheijang, China we tested the role of usage of cardiovascular, antidiabetic and other medications on risk and severity of COVID 19. Analyses were adjusted for age, sex and BMI and for presence of relevant comorbidities. Individuals with hypertension taking calcium channel blockers had significantly increased risk [odds ratio (OR)= 1.73 (95% CI 1.2-2.3)] of manifesting symptoms of COVID-19 whereas those taking angiotensin receptor blockers and diuretics had significantly lower disease risk (OR=0.22; 95%CI 0.15-0.30 and OR=0.30; 95%CI 0.19-0.58 respectively). Among those with type 2 diabetes, dipeptidyl peptidase-4 inhibitors (OR= 6.02; 95% CI 2.3- 15.5) and insulin (OR= 2.71; 95% CI 1.6-5.5) were more and glucosidase inhibitors were less prevalent (OR= 0.11; 95% CI 0.1-0.3) among with COVID-19 patients. Drugs used in the treatment of hypertension and diabetes influence the risk of development of COVID-19, but, not its severity
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data