189 research outputs found
Learning Efficient Convolutional Networks through Irregular Convolutional Kernels
As deep neural networks are increasingly used in applications suited for
low-power devices, a fundamental dilemma becomes apparent: the trend is to grow
models to absorb increasing data that gives rise to memory intensive; however
low-power devices are designed with very limited memory that can not store
large models. Parameters pruning is critical for deep model deployment on
low-power devices. Existing efforts mainly focus on designing highly efficient
structures or pruning redundant connections for networks. They are usually
sensitive to the tasks or relay on dedicated and expensive hashing storage
strategies. In this work, we introduce a novel approach for achieving a
lightweight model from the views of reconstructing the structure of
convolutional kernels and efficient storage. Our approach transforms a
traditional square convolution kernel to line segments, and automatically learn
a proper strategy for equipping these line segments to model diverse features.
The experimental results indicate that our approach can massively reduce the
number of parameters (pruned 69% on DenseNet-40) and calculations (pruned 59%
on DenseNet-40) while maintaining acceptable performance (only lose less than
2% accuracy)
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark
Gait depicts individuals' unique and distinguishing walking patterns and has
become one of the most promising biometric features for human identification.
As a fine-grained recognition task, gait recognition is easily affected by many
factors and usually requires a large amount of completely annotated data that
is costly and insatiable. This paper proposes a large-scale self-supervised
benchmark for gait recognition with contrastive learning, aiming to learn the
general gait representation from massive unlabelled walking videos for
practical applications via offering informative walking priors and diverse
real-world variations. Specifically, we collect a large-scale unlabelled gait
dataset GaitLU-1M consisting of 1.02M walking sequences and propose a
conceptually simple yet empirically powerful baseline model GaitSSB.
Experimentally, we evaluate the pre-trained model on four widely-used gait
benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer
learning. The unsupervised results are comparable to or even better than the
early model-based and GEI-based methods. After transfer learning, our method
outperforms existing methods by a large margin in most cases. Theoretically, we
discuss the critical issues for gait-specific contrastive framework and present
some insights for further study. As far as we know, GaitLU-1M is the first
large-scale unlabelled gait dataset, and GaitSSB is the first method that
achieves remarkable unsupervised results on the aforementioned benchmarks. The
source code of GaitSSB will be integrated into OpenGait which is available at
https://github.com/ShiqiYu/OpenGait
GPGait: Generalized Pose-based Gait Recognition
Recent works on pose-based gait recognition have demonstrated the potential
of using such simple information to achieve results comparable to
silhouette-based methods. However, the generalization ability of pose-based
methods on different datasets is undesirably inferior to that of
silhouette-based ones, which has received little attention but hinders the
application of these methods in real-world scenarios. To improve the
generalization ability of pose-based methods across datasets, we propose a
\textbf{G}eneralized \textbf{P}ose-based \textbf{Gait} recognition
(\textbf{GPGait}) framework. First, a Human-Oriented Transformation (HOT) and a
series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified
pose representation with discriminative multi-features. Then, given the slight
variations in the unified representation after HOT and HOD, it becomes crucial
for the network to extract local-global relationships between the keypoints. To
this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to
enable efficient graph partition and local-global spatial feature extraction.
Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose,
Gait3D and GREW, show that our model demonstrates better and more stable
cross-domain capabilities compared to existing skeleton-based methods,
achieving comparable recognition results to silhouette-based ones. Code is
available at https://github.com/BNU-IVC/FastPoseGait.Comment: ICCV Camera Read
FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait Recognition
We present FastPoseGait, an open-source toolbox for pose-based gait
recognition based on PyTorch. Our toolbox supports a set of cutting-edge
pose-based gait recognition algorithms and a variety of related benchmarks.
Unlike other pose-based projects that focus on a single algorithm, FastPoseGait
integrates several state-of-the-art (SOTA) algorithms under a unified
framework, incorporating both the latest advancements and best practices to
ease the comparison of effectiveness and efficiency. In addition, to promote
future research on pose-based gait recognition, we provide numerous pre-trained
models and detailed benchmark results, which offer valuable insights and serve
as a reference for further investigations. By leveraging the highly modular
structure and diverse methods offered by FastPoseGait, researchers can quickly
delve into pose-based gait recognition and promote development in the field. In
this paper, we outline various features of this toolbox, aiming that our
toolbox and benchmarks can further foster collaboration, facilitate
reproducibility, and encourage the development of innovative algorithms for
pose-based gait recognition. FastPoseGait is available at
https://github.com//BNU-IVC/FastPoseGait and is actively maintained. We will
continue updating this report as we add new features.Comment: 10 pages, 4 figure
Towards More Efficient Depression Risk Recognition via Gait
Depression, a highly prevalent mental illness, affects over 280 million
individuals worldwide. Early detection and timely intervention are crucial for
promoting remission, preventing relapse, and alleviating the emotional and
financial burdens associated with depression. However, patients with depression
often go undiagnosed in the primary care setting. Unlike many physiological
illnesses, depression lacks objective indicators for recognizing depression
risk, and existing methods for depression risk recognition are time-consuming
and often encounter a shortage of trained medical professionals. The
correlation between gait and depression risk has been empirically established.
Gait can serve as a promising objective biomarker, offering the advantage of
efficient and convenient data collection. However, current methods for
recognizing depression risk based on gait have only been validated on small,
private datasets, lacking large-scale publicly available datasets for research
purposes. Additionally, these methods are primarily limited to hand-crafted
approaches. Gait is a complex form of motion, and hand-crafted gait features
often only capture a fraction of the intricate associations between gait and
depression risk. Therefore, this study first constructs a large-scale gait
database, encompassing over 1,200 individuals, 40,000 gait sequences, and
covering six perspectives and three types of attire. Two commonly used
psychological scales are provided as depression risk annotations. Subsequently,
a deep learning-based depression risk recognition model is proposed, overcoming
the limitations of hand-crafted approaches. Through experiments conducted on
the constructed large-scale database, the effectiveness of the proposed method
is validated, and numerous instructive insights are presented in the paper,
highlighting the significant potential of gait-based depression risk
recognition
Data Decomposition and Spatial Mixture Modeling for Part Based Model
Abstract. This paper presents a system of data decomposition and spa-tial mixture modeling for part based models. Recently, many enhanced part based models (with e.g., multiple features, more components or parts) have been proposed. Nevertheless, those enhanced models bring high computation cost together with the risk of over-fitting. To tackle this problem, we propose a data decomposition method for part based models which not only accelerates training and testing process but also improves the performance on average. Besides, the original part based model uses a strict rigid structural model to describe the distribution of each part location. It is not “deformable ” enough, especially for those instances with different viewpoints or poses in the same aspect ratio. To address this problem, we present a novel spatial mixture modeling method. The spatial mixture embedded model is then integrated into the proposed data decomposition framework. We evaluate our system on the challenging PASCAL VOC2007 and PASCAL VOC2010 datasets, demonstrating the state-of-the-art performance compared with other re-lated methods in terms of accuracy and efficiency.
Detection of copy number variations and their effects in Chinese bulls
BACKGROUND: Copy number variations (CNVs) are a main source of genomic structural variations underlying animal evolution and production traits. Here, with one pure-blooded Angus bull as reference, we describe a genome-wide analysis of CNVs based on comparative genomic hybridization arrays in 29 Chinese domesticated bulls and examined their effects on gene expression and cattle growth traits. RESULTS: We identified 486 copy number variable regions (CNVRs), covering 2.45% of the bovine genome, in 24 taurine (Bos taurus), together with 161 ones in 2 yaks (Bos grunniens) and 163 ones in 3 buffaloes (Bubalus bubalis). Totally, we discovered 605 integrated CNVRs, with more “loss” events than both “gain” and “both” ones, and clearly clustered them into three cattle groups. Interestingly, we confirmed their uneven distributions across chromosomes, and the differences of mitochondrion DNA copy number (gain: taurine, loss: yak & buffalo). Furthermore, we confirmed approximately 41.8% (253/605) and 70.6% (427/605) CNVRs span cattle genes and quantitative trait loci (QTLs), respectively. Finally, we confirmed 6 CNVRs in 9 chosen ones by using quantitative PCR, and further demonstrated that CNVR22 had significantly negative effects on expression of PLA2G2D gene, and both CNVR22 and CNVR310 were associated with body measurements in Chinese cattle, suggesting their key effects on gene expression and cattle traits. CONCLUSIONS: The results advanced our understanding of CNV as an important genomic structural variation in taurine, yak and buffalo. This study provides a highly valuable resource for Chinese cattle’s evolution and breeding researches. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-480) contains supplementary material, which is available to authorized users
Case report: A novel case of COVID-19 triggered tumefactive demyelinating lesions in one multiple sclerosis patient
The epidemic of COVID-19 is mainly manifested by respiratory symptoms caused by SARS-CoV-2 infection. Recently, reports of central nervous system diseases caused or aggravated by SARS-CoV-2 infection are also increasing. Thus, the COVID-19 pandemic poses an unprecedented challenge to the diagnosis and management of neurological disorders, especially to those diseases which have overlapping clinical and radiologic features with each other. In this study, a 31-year-old female patient had been diagnosed with relapsing–remitting multiple sclerosis (RRMS) initially and subsequently developed tumefactive demyelinating lesions (TDLs) following an infection with SARS-CoV-2. After immunotherapy (glucocorticoid pulses), a significant improvement was observed in her both clinical and radiological characteristics. The patient was started on disease-modifying therapy (DMT) with teriflunomide after cessation of oral glucocorticoids. Following two months of DMT treatment, the imaging follow-up revealed that the patient’s condition continued to deteriorate. This case was characterized by the transformation of a multiple sclerosis patient (MS) infected with SARS-CoV-2 into TDLs and the ineffectiveness of DMT treatment, which added complexity to its diagnosis and treatment. The case also gave us a hint that SARS-CoV-2 has a potential contributory role in inducing or exacerbating demyelinating diseases of the central nervous system that warrants further investigation
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