The paper reports first-place results on the CVPR2025 EgoExo4D hand pose, body pose, and proficiency estimation challenges using hybrid transformer-CNN and multimodal fusion architectures.
ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis
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abstract
Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has proved challenging. This study addresses the automation of resource-intensive and time-consuming process of manually detecting stenotic lesions in coronary arteries in X-ray coronary angiography images. To overcome this challenge, we employ a specialized Convnext-V2 backbone based Mask RCNN model pre-trained for instance segmentation tasks. Our empirical findings affirm that the proposed model exhibits commendable performance in identifying stenotic lesions. Notably, our approach achieves a substantial F1 score of 0.5353 in this demanding task, underscoring its effectiveness in streamlining this intensive process.
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PCIE_Pose Solution for EgoExo4D Pose and Proficiency Estimation Challenge
The paper reports first-place results on the CVPR2025 EgoExo4D hand pose, body pose, and proficiency estimation challenges using hybrid transformer-CNN and multimodal fusion architectures.