1,060 research outputs found
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network
Unsupervised approaches for video anomaly detection may not perform as good
as supervised approaches. However, learning unknown types of anomalies using an
unsupervised approach is more practical than a supervised approach as
annotation is an extra burden. In this paper, we use isolation tree-based
unsupervised clustering to partition the deep feature space of the video
segments. The RGB- stream generates a pseudo anomaly score and the flow stream
generates a pseudo dynamicity score of a video segment. These scores are then
fused using a majority voting scheme to generate preliminary bags of positive
and negative segments. However, these bags may not be accurate as the scores
are generated only using the current segment which does not represent the
global behavior of a typical anomalous event. We then use a refinement strategy
based on a cross-branch feed-forward network designed using a popular I3D
network to refine both scores. The bags are then refined through a segment
re-mapping strategy. The intuition of adding the dynamicity score of a segment
with the anomaly score is to enhance the quality of the evidence. The method
has been evaluated on three popular video anomaly datasets, i.e., UCF-Crime,
CCTV-Fights, and UBI-Fights. Experimental results reveal that the proposed
framework achieves competitive accuracy as compared to the state-of-the-art
video anomaly detection methods.Comment: 10 pages, 8 figures, and 4 tables. (ACCEPTED AT WACV 2023
Person Re-identification in Videos by Analyzing Spatio-temporal Tubes
Typical person re-identification frameworks search for k best matches in a gallery of images that are often collected in varying conditions. The gallery usually contains image sequences for video re-identification applications. However, such a process is time consuming as video re-identification involves carrying out the matching process multiple times. In this paper, we propose a new method that extracts spatio-temporal frame sequences or tubes of moving persons and performs the re-identification in quick time. Initially, we apply a binary classifier to remove noisy images from the input query tube. In the next step, we use a key-pose detection-based query minimization technique. Finally, a hierarchical re-identification framework is proposed and used to rank the output tubes. Experiments with publicly available video re-identification datasets reveal that our framework is better than existing methods. It ranks the tubes with an average increase in the CMC accuracy of 6-8% across multiple datasets. Also, our method significantly reduces the number of false positives. A new video re-identification dataset, named Tube-based Re-identification Video Dataset (TRiViD), has been prepared with an aim to help the re-identification research community
MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation
We propose a scene-level inverse rendering framework that uses multi-view
images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying
lighting. Because multi-view images provide a variety of information about the
scene, multi-view images in object-level inverse rendering have been taken for
granted. However, owing to the absence of multi-view HDR synthetic dataset,
scene-level inverse rendering has mainly been studied using single-view image.
We were able to successfully perform scene-level inverse rendering using
multi-view images by expanding OpenRooms dataset and designing efficient
pipelines to handle multi-view images, and splitting spatially-varying
lighting. Our experiments show that the proposed method not only achieves
better performance than single-view-based methods, but also achieves robust
performance on unseen real-world scene. Also, our sophisticated 3D
spatially-varying lighting volume allows for photorealistic object insertion in
any 3D location.Comment: Accepted by CVPR 2023; Project Page is
https://bring728.github.io/mair.project
The impact of baryonic physics and massive neutrinos on weak lensing peak statistics
We study the impact of baryonic processes and massive neutrinos on weak lensing peak statistics that can be used to constrain cosmological parameters. We use the BAHAMAS suite of cosmological simulations, which self-consistently include baryonic processes and the effect of massive neutrino free-streaming on the evolution of structure formation. We construct synthetic weak lensing catalogues by ray-tracing through light-cones, and use the aperture mass statistic for the analysis. The peaks detected on the maps reflect the cumulative signal from massive bound objects and general large-scale structure. We present the first study of weak lensing peaks in simulations that include both baryonic physics and massive neutrinos (summed neutrino mass 0.06, 0.12, 0.24, and 0.48 eV assuming normal hierarchy), so that the uncertainty due to physics beyond the gravity of dark matter can be factored into constraints on cosmological models. Assuming a fiducial model of baryonic physics, we also investigate the correlation between peaks and massive haloes, over a range of summed neutrino mass values. As higher neutrino mass tends to suppress the formation of massive structures in the Universe, the halo mass function and lensing peak counts are therefore modified as a function of . Over most of the S/N range, the impact of fiducial baryonic physics is greater (less) than neutrinos for 0.06 and 0.12 (0.24 and 0.48) eV models. Both baryonic physics and massive neutrinos should be accounted for when deriving cosmological parameters from weak lensing observations
On stable higher spin states in Heterotic String Theories
We study properties of 1/2 BPS Higher Spin states in heterotic
compactifications with extended supersymmetry. We also analyze non BPS Higher
Spin states and give explicit expressions for physical vertex operators of the
first two massive levels. We then study on-shell tri-linear couplings of these
Higher Spin states and confirm that BPS states with arbitrary spin cannot decay
into lower spin states in perturbation theory. Finally, we consider scattering
of vector bosons off higher spin BPS states and extract form factors and
polarization effects in various limits.Comment: 38 page
A compendium and functional characterization of mammalian genes involved in adaptation to Arctic or Antarctic environments
Many mammals are well adapted to surviving in extremely cold environments. These species have likely accumulated genetic changes that help them efficiently cope with low temperatures. It is not known whether the same genes related to cold adaptation in one species would be under selection in another species. The aims of this study therefore were: to create a compendium of mammalian genes related to adaptations to a low temperature environment; to identify genes related to cold tolerance that have been subjected to independent positive selection in several species; to determine promising candidate genes/pathways/organs for further empirical research on cold adaptation in mammals
FRASS: the web-server for RNA structural comparison
<p>Abstract</p> <p>Background</p> <p>The impressive increase of novel RNA structures, during the past few years, demands automated methods for structure comparison. While many algorithms handle only small motifs, few techniques, developed in recent years, (ARTS, DIAL, SARA, SARSA, and LaJolla) are available for the structural comparison of large and intact RNA molecules.</p> <p>Results</p> <p>The FRASS web-server represents a RNA chain with its Gauss integrals and allows one to compare structures of RNA chains and to find similar entries in a database derived from the Protein Data Bank. We observed that FRASS scores correlate well with the ARTS and LaJolla similarity scores. Moreover, the-web server can also reproduce satisfactorily the DARTS classification of RNA 3D structures and the classification of the SCOR functions that was obtained by the SARA method.</p> <p>Conclusions</p> <p>The FRASS web-server can be easily used to detect relationships among RNA molecules and to scan efficiently the rapidly enlarging structural databases.</p
Prediction of Thrombectomy Functional Outcomes using Multimodal Data
Recent randomised clinical trials have shown that patients with ischaemic
stroke {due to occlusion of a large intracranial blood vessel} benefit from
endovascular thrombectomy. However, predicting outcome of treatment in an
individual patient remains a challenge. We propose a novel deep learning
approach to directly exploit multimodal data (clinical metadata information,
imaging data, and imaging biomarkers extracted from images) to estimate the
success of endovascular treatment. We incorporate an attention mechanism in our
architecture to model global feature inter-dependencies, both channel-wise and
spatially. We perform comparative experiments using unimodal and multimodal
data, to predict functional outcome (modified Rankin Scale score, mRS) and
achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy
for individual mRS scores.Comment: Accepted at Medical Image Understanding and Analysis (MIUA) 202
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