Improving Adversarial Robustness Against Universal Patch Attacks Through Feature Norm Suppressing.

Universal adversarial patch attacks, which are readily implemented, have been validated to be able to fool real-world deep convolutional neural networks (CNNs), posing a serious threat to practical computer vision systems based on CNNs. Unfortunately, current defending approaches are severely understudied facing the following problems. Patch detection-based methods suffer from dramatic performance drops against white-box or adaptive attacks since they rely heavily on empirical clues.

Boosting Monocular 3D Human Pose Estimation With Part Aware Attention.

Monocular 3D human pose estimation is challenging due to depth ambiguity. Convolution-based and Graph-Convolution-based methods have been developed to extract 3D information from temporal cues in motion videos. Typically, in the lifting-based methods, most recent works adopt the transformer to model the temporal relationship of 2D keypoint sequences.

Video Based Shuffling Step Detection for Parkinsonian Patients Using 3D Convolution.

Parkinson's Disease (PD) is a common neurodegenerative disease which impacts millions of people around the world. In clinical treatments, freezing of gait (FoG) is used as the typical symptom to assess PD patients' condition. Currently, the assessment of FoG is usually performed through live observation or video analysis by doctors.

Image Captioning with End-to-end Attribute Detection and Subsequent Attributes Prediction.

Semantic attention has been shown to be effective in improving the performance of image captioning. The core of semantic attention based methods is to drive the model to attend to semantically important words, or attributes. In previous works, the attribute detector and the captioning network are usually independent, leading to the insufficient usage of the semantic information.

Vision-Based Method for Automatic Quantification of Parkinsonian Bradykinesia.

Non-volitional discontinuation of motion, namely bradykinesia, is a common motor symptom among patients with Parkinson's disease (PD). Evaluating bradykinesia severity is an important part of clinical examinations on PD patients in both diagnosis and monitoring phases. However, subjective evaluations from different clinicians often show low consistency.