202 research outputs found
UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance
Object detection has long been a topic of high interest in computer vision
literature. Motivated by the fact that annotating data for the multi-object
tracking (MOT) problem is immensely expensive, recent studies have turned their
attention to the unsupervised learning setting. In this paper, we push forward
the state-of-the-art performance of unsupervised MOT methods by proposing
UnsMOT, a novel framework that explicitly combines the appearance and motion
features of objects with geometric information to provide more accurate
tracking. Specifically, we first extract the appearance and motion features
using CNN and RNN models, respectively. Then, we construct a graph of objects
based on their relative distances in a frame, which is fed into a GNN model
together with CNN features to output geometric embedding of objects optimized
using an unsupervised loss function. Finally, associations between objects are
found by matching not only similar extracted features but also geometric
embedding of detections and tracklets. Experimental results show remarkable
performance in terms of HOTA, IDF1, and MOTA metrics in comparison with
state-of-the-art methods
HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
We consider the challenging task of training models for image-to-video
deblurring, which aims to recover a sequence of sharp images corresponding to a
given blurry image input. A critical issue disturbing the training of an
image-to-video model is the ambiguity of the frame ordering since both the
forward and backward sequences are plausible solutions. This paper proposes an
effective self-supervised ordering scheme that allows training high-quality
image-to-video deblurring models. Unlike previous methods that rely on
order-invariant losses, we assign an explicit order for each video sequence,
thus avoiding the order-ambiguity issue. Specifically, we map each video
sequence to a vector in a latent high-dimensional space so that there exists a
hyperplane such that for every video sequence, the vectors extracted from it
and its reversed sequence are on different sides of the hyperplane. The side of
the vectors will be used to define the order of the corresponding sequence.
Last but not least, we propose a real-image dataset for the image-to-video
deblurring problem that covers a variety of popular domains, including face,
hand, and street. Extensive experimental results confirm the effectiveness of
our method. Code and data are available at
https://github.com/VinAIResearch/HyperCUT.gitComment: Accepted to CVPR 202
Encourage Fathers' Use of Paternal Leave in Vietnam: the Labor Contract Characteristics and Fathers' Behavior
Vietnam reformed the maternity regime in 2014, in which male employees currently paying social insurance premiums whose wives give birth to children are entitled to a maternity leave of from 5 to 14 working days. This is widely regarded as a landmark reform that has shifted the Vietnam maternity regime toward a model that is more supportive of the compatibility of work and family life. In this paper, we show that fathers' leave-taking was only about 5,2% of total male employees currently paying social insurance premiums whose wives give birth, even when fathers are on leave period, they spend very little time with their children; despite drastic changes in gender norms in Vietnam, as well as short periods of leave, may have long-lasting effects on fathersâ involvement in newborn care and housework. We also find that groups of fathers, who are cadres and civil servants, as well as those with permanent working contracts profited more strongly from changing their behavior, while self-employment and temporary work lower fathersâ chances of taking leave. Finally, we make some compelling proposals to increase the number of fathers taking parental leave
Encourage Fathers' Use of Paternal Leave in Vietnam: the Labor Contract Characteristics and Fathers' Behavior
Vietnam reformed the maternity regime in 2014, in which male employees currently paying social insurance premiums whose wives give birth to children are entitled to a maternity leave of from 5 to 14 working days. This is widely regarded as a landmark reform that has shifted the Vietnam maternity regime toward a model that is more supportive of the compatibility of work and family life. In this paper, we show that fathers' leave-taking was only about 5,2% of total male employees currently paying social insurance premiums whose wives give birth, even when fathers are on leave period, they spend very little time with their children; despite drastic changes in gender norms in Vietnam, as well as short periods of leave, may have long-lasting effects on fathersâ involvement in newborn care and housework. We also find that groups of fathers, who are cadres and civil servants, as well as those with permanent working contracts profited more strongly from changing their behavior, while self-employment and temporary work lower fathersâ chances of taking leave. Finally, we make some compelling proposals to increase the number of fathers taking parental leave
Federated Few-shot Learning for Cough Classification with Edge Devices
Automatically classifying cough sounds is one of the most critical tasks for
the diagnosis and treatment of respiratory diseases. However, collecting a huge
amount of labeled cough dataset is challenging mainly due to high laborious
expenses, data scarcity, and privacy concerns. In this work, our aim is to
develop a framework that can effectively perform cough classification even in
situations when enormous cough data is not available, while also addressing
privacy concerns. Specifically, we formulate a new problem to tackle these
challenges and adopt few-shot learning and federated learning to design a novel
framework, termed F2LCough, for solving the newly formulated problem. We
illustrate the superiority of our method compared with other approaches on
COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average
F1-Score of 86%. Our results show the feasibility of few-shot learning combined
with federated learning to build a classification model of cough sounds. This
new methodology is able to classify cough sounds in data-scarce situations and
maintain privacy properties. The outcomes of this work can be a fundamental
framework for building support systems for the detection and diagnosis of
cough-related diseases.Comment: 21 pages, 5 figure
Context-aware Knowledge-based Systems: A Literature Review
Context awareness systems, a subcategory of intelligent systems, are concerned with suggesting relevant products/services to users' situations as smart services. One key element for improving smart servicesâ quality is to organize and manipulate contextual data in an appropriate manner to facilitate knowledge generation from these data. In this light, a knowledge-based approach, can be used as a key component in context-aware systems. Context awareness and knowledge-based systems, in fact, have been gaining prominence in their respective domains for decades. However, few studies have focused on how to reconcile the two fields to maximize the benefits of each field. For this reason, the objective of this paper is to present a literature review of how context-aware systems, with a focus on the knowledge-based approach, have recently been conceptualized to promote further research in this area. In the end, the implications and current challenges of the study will be discussed
Chemical constituents from the leaves of Styrax argentifolius H.L. Li and their antioxidative activity
Searching for bioactive agents from medicinal plants, the phytochemical investigation on the EtOAc extract of the Vietnamese Styrax argentifoliusĂ leaves has resulted in the isolation and structural determination of five compounds, including one nor-neolignan egonoic acid (1), one lignan (+)-pinoresinol (2), one sterol (20R)-3ĂÂČ-hydroxysitgmasta-5,22-dien-7-one (3), and two triterpenoids lupeol (4), and 2ñ,3ñ,24-trihydroxy-urs-12-en-28-oic acidĂ (5). The chemical structures of these secondary metabolites were elucidated by NMR and MS spectral data. All isolated compounds were first observed in S. argentifoliusĂ species. Sterol 3Ă and triterpenoid 5Ă were detected in genus StyraxĂ for the first time. With the IC50Ă value of 19.10 Ă”g/mL, compound 2Ă possessed the strong activity in DPPH radical scavenging assay
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