33 research outputs found
Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks
Deep learning models have achieved excellent recognition results on
large-scale video benchmarks. However, they perform poorly when applied to
videos with rare scenes or objects, primarily due to the bias of existing video
datasets. We tackle this problem from two different angles: algorithm and
dataset. From the perspective of algorithms, we propose Spatial-aware
Multi-Aspect Debiasing (SMAD), which incorporates both explicit debiasing with
multi-aspect adversarial training and implicit debiasing with the spatial
actionness reweighting module, to learn a more generic representation invariant
to non-action aspects. To neutralize the intrinsic dataset bias, we propose
OmniDebias to leverage web data for joint training selectively, which can
achieve higher performance with far fewer web data. To verify the
effectiveness, we establish evaluation protocols and perform extensive
experiments on both re-distributed splits of existing datasets and a new
evaluation dataset focusing on the action with rare scenes. We also show that
the debiased representation can generalize better when transferred to other
datasets and tasks.Comment: ECCVW 202
Exploration of the impact of demographic changes on life insurance consumption: empirical analysis based on Shanghai Cooperation Organization
Based on the panel data of eight member states of Shanghai
Cooperation Organization (SCO) from 1996 to 2019, this study
explores the impact of demographic changes on life insurance
consumption in SCO member countries under the framework of
static panel model and dynamic panel model. And the study analyzes
the heterogeneity of religious division and different aging
degrees. The empirical results show that both old-age dependency
ratio and teenager dependency ratio have positive impacts
on life insurance consumption in the SCO countries. Besides, the
current consumption of ordinary life insurance significantly stimulates
the future consumption of ordinary life insurance.
Furthermore, demographic changes have heterogeneous impacts
on life insurance consumption in terms of different religions and
different degrees of aging. Our findings provide managerial implications
for insurance companies that carry out life insurance business
in SCO member states. Insurance companies should consider
the policyholders’ life insurance consumption in accordance with
demographic changes of both old-age dependency ratio and
teenager dependency ratio, and also take differentiated life insurance
sales strategies according to different degrees of aging and
whether the residents believe in Islam
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild
We present SkeleTR, a new framework for skeleton-based action recognition. In
contrast to prior work, which focuses mainly on controlled environments, we
target more general scenarios that typically involve a variable number of
people and various forms of interaction between people. SkeleTR works with a
two-stage paradigm. It first models the intra-person skeleton dynamics for each
skeleton sequence with graph convolutions, and then uses stacked Transformer
encoders to capture person interactions that are important for action
recognition in general scenarios. To mitigate the negative impact of inaccurate
skeleton associations, SkeleTR takes relative short skeleton sequences as input
and increases the number of sequences. As a unified solution, SkeleTR can be
directly applied to multiple skeleton-based action tasks, including video-level
action classification, instance-level action detection, and group-level
activity recognition. It also enables transfer learning and joint training
across different action tasks and datasets, which result in performance
improvement. When evaluated on various skeleton-based action recognition
benchmarks, SkeleTR achieves the state-of-the-art performance.Comment: ICCV 202
Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study
BackgroundShort-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH.MethodsThis study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP).ResultsA total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77–0.86), high accuracy for 0.74 (95% CI: 0.72–0.76), sensitivity 0.78 (95% CI: 0.69–0.87), and specificity 0.74 (95% CI: 0.72–0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results.ConclusionsThis study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH