Knowledge, attitude, and practices on COVID-19 prevention and diagnosis among medical workers in the radiology department: A multicenter cross-sectional study in China.

Objective: The aim of this study was to investigate the knowledge, attitude, and practice (KAP) of medical workers in the radiology department toward the prevention and diagnosis of COVID-19.

Methods: This multicenter cross-sectional study was conducted among medical workers in the radiology department of 17 hospitals between March and June 2022.

Results: A total of 324 medical workers were enrolled.

Parameter-Efficient Person Re-Identification in the 3D Space.

People live in a 3D world. However, existing works on person re-identification (re-id) mostly consider the semantic representation learning in a 2D space, intrinsically limiting the understanding of people. In this work, we address this limitation by exploring the prior knowledge of the 3D body structure.

Adaptive Boosting for Domain Adaptation: Toward Robust Predictions in Scene Segmentation.

Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i.e., target domain.

Joint Representation Learning and Keypoint Detection for Cross-View Geo-Localization.

In this paper, we study the cross-view geo-localization problem to match images from different viewpoints. The key motivation underpinning this task is to learn a discriminative viewpoint-invariant visual representation. Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.

Soft Person Reidentification Network Pruning via Blockwise Adjacent Filter Decaying.

Deep learning has shown significant successes in person reidentification (re-id) tasks. However, most existing works focus on discriminative feature learning and impose complex neural networks, suffering from low inference efficiency. In fact, feature extraction time is also crucial for real-world applications and lightweight models are needed.

Unsupervised Eyeglasses Removal in the Wild.

Eyeglasses removal is challenging in removing different kinds of eyeglasses, e.g., rimless glasses, full-rim glasses, and sunglasses, and recovering appropriate eyes.

Multi-pseudo Regularized Label for Generated Data in Person Re-Identification.

Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images (i.e.

CamStyle: A Novel Data Augmentation Method for Person Re-Identification.

Person re-identification (re-ID) is a cross-camera retrieval task that suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle).