33 research outputs found
Road User Detection in Videos
Successive frames of a video are highly redundant, and the most popular
object detection methods do not take advantage of this fact. Using multiple
consecutive frames can improve detection of small objects or difficult examples
and can improve speed and detection consistency in a video sequence, for
instance by interpolating features between frames. In this work, a novel
approach is introduced to perform online video object detection using two
consecutive frames of video sequences involving road users. Two new models,
RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the
concatenation of a target frame with a preceding frame, and the concatenation
of the optical flow with the target frame. The models are trained and evaluated
on three public datasets. Experiments show that using a preceding frame
improves performance over single frame detectors, but using explicit optical
flow usually does not
Road User Detection in Videos
Successive frames of a video are highly redundant, and the most popular
object detection methods do not take advantage of this fact. Using multiple
consecutive frames can improve detection of small objects or difficult examples
and can improve speed and detection consistency in a video sequence, for
instance by interpolating features between frames. In this work, a novel
approach is introduced to perform online video object detection using two
consecutive frames of video sequences involving road users. Two new models,
RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the
concatenation of a target frame with a preceding frame, and the concatenation
of the optical flow with the target frame. The models are trained and evaluated
on three public datasets. Experiments show that using a preceding frame
improves performance over single frame detectors, but using explicit optical
flow usually does not
RN-VID: A Feature Fusion Architecture for Video Object Detection
Consecutive frames in a video are highly redundant. Therefore, to perform the
task of video object detection, executing single frame detectors on every frame
without reusing any information is quite wasteful. It is with this idea in mind
that we propose RN-VID (standing for RetinaNet-VIDeo), a novel approach to
video object detection. Our contributions are twofold. First, we propose a new
architecture that allows the usage of information from nearby frames to enhance
feature maps. Second, we propose a novel module to merge feature maps of same
dimensions using re-ordering of channels and 1 x 1 convolutions. We then
demonstrate that RN-VID achieves better mean average precision (mAP) than
corresponding single frame detectors with little additional cost during
inference
The genomics of heart failure: design and rationale of the HERMES consortium
Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of >1.10 for common variants (allele frequency > 0.05) and >1.20 for low-frequency variants (allele frequency 0.01-0.05) at P Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.</p
The genomics of heart failure: design and rationale of the HERMES consortium
Aims: The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. Methods and results: The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genomeâwide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median followâup following heart failure diagnosis ranged from 2 to 116 months. Fortyânine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34â90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of â„1.10 for common variants (allele frequency â„ 0.05) and â„1.20 for lowâfrequency variants (allele frequency 0.01â0.05) at P < 5 Ă 10â8 under an additive genetic model. Conclusions: HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction
CD8 T-cell ability to exert immunodomination correlates with T-cell receptor: Epitope association rate
AbstractWhen presented alone, H7a and HY antigens elicit CD8 T-cell responses of similar amplitude, but H7a totally abrogates the response to HY when both antigens are presented on the same antigen-presenting cell. We found that H7a- and HY-specific T-cell precursors had similar frequencies in nonimmune mice and expressed similar levels of CD5. The H7a-specific CD8 T-cell repertoire harvested at the time of primary response showed highly restricted T-cell receptor (TCR) diversity. Furthermore, T cells specific for H7a and HY expressed equivalent levels of CD8 and TCR and displayed similar tetramer decay rates. The key difference was that anti-H7a T cells exhibited a much more rapid TCR:epitope on-rate than anti-HY T cells. Coupled with evidence that primed CD8 T cells limit the duration of antigen presentation by killing or inactivating antigen-presenting cells, our data support a novel and simple model for immunodomination: the main feature of T cells that exert immunodomination is that, compared with other T cells, they are functionally primed after a shorter duration of antigen presentation
SpotNet: Self-Attention Multi-Task Network for Object Detection
Humans are very good at directing their visual attention toward relevant
areas when they search for different types of objects. For instance, when we
search for cars, we will look at the streets, not at the top of buildings. The
motivation of this paper is to train a network to do the same via a multi-task
learning approach. To train visual attention, we produce foreground/background
segmentation labels in a semi-supervised way, using background subtraction or
optical flow. Using these labels, we train an object detection model to produce
foreground/background segmentation maps as well as bounding boxes while sharing
most model parameters. We use those segmentation maps inside the network as a
self-attention mechanism to weight the feature map used to produce the bounding
boxes, decreasing the signal of non-relevant areas. We show that by using this
method, we obtain a significant mAP improvement on two traffic surveillance
datasets, with state-of-the-art results on both UA-DETRAC and UAVDT
PolyTrack : tracking with bounding polygons
In this paper, we present a novel method called PolyTrack for fast
multi-object tracking and segmentation using bounding polygons. Polytrack
detects objects by producing heatmaps of their center keypoint. For each of
them, a rough segmentation is done by computing a bounding polygon over each
instance instead of the traditional bounding box. Tracking is done by taking
two consecutive frames as input and computing a center offset for each object
detected in the first frame to predict its location in the second frame. A
Kalman filter is also applied to reduce the number of ID switches. Since our
target application is automated driving systems, we apply our method on urban
environment videos. We trained and evaluated PolyTrack on the MOTS and
KITTIMOTS datasets. Results show that tracking polygons can be a good
alternative to bounding box and mask tracking. The code of PolyTrack is
available at https://github.com/gafaua/PolyTrack.Comment: NeurIPS 2021 Machine Learning for Autonomous Driving Worksho