SeBIR: Semantic-guided burst image restoration.

Burst image restoration methods offer the possibility of recovering faithful scene details from multiple low-quality snapshots captured by hand-held devices in adverse scenarios, thereby attracting increasing attention in recent years. However, individual frames in a burst typically suffer from inter-frame misalignments, leading to ghosting artifacts. Besides, existing methods indiscriminately handle all burst frames, struggling to seamlessly remove the corrupted information due to the neglect of multi-frame spatio-temporal varying degradation.

ESAMask: Real-Time Instance Segmentation Fused with Efficient Sparse Attention.

Instance segmentation is a challenging task in computer vision, as it requires distinguishing objects and predicting dense areas. Currently, segmentation models based on complex designs and large parameters have achieved remarkable accuracy. However, from a practical standpoint, achieving a balance between accuracy and speed is even more desirable.

Self-supervised learning-based Multi-Scale feature Fusion Network for survival analysis from whole slide images.

Understanding prognosis and mortality is critical for evaluating the treatment plan of patients. Advances in digital pathology and deep learning techniques have made it practical to perform survival analysis in whole slide images (WSIs). Current methods are usually based on a multi-stage framework which includes patch sampling, feature extraction and prediction.

Uncertainty Guided Multi-Scale Attention Network for Raindrop Removal From a Single Image.

Raindrops adhered to a glass window or camera lens appear in various blurring degrees and resolutions due to the difference in the degrees of raindrops aggregation. The removal of raindrops from a rainy image remains a challenging task because of the density and diversity of raindrops. The abundant location and blur level information are strong prior guide to the task of raindrop removal.

Feature Selection Based on Neighborhood Self-Information.

The concept of dependency in a neighborhood rough set model is an important evaluation function for the feature selection. This function considers only the classification information contained in the lower approximation of the decision while ignoring the upper approximation. In this paper, we construct a class of uncertainty measures: decision self-information for the feature selection.

Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data.

The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC.