Learning a single static convolutional kernel in each convolutional layer is the common
training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent …

In this paper, we tackle the problem of few-shot class incremental learning (FSCIL). FSCIL
aims to incrementally learn new classes with only a few samples in each class. Most existing …

The increasing popularity of attention mechanisms in deep learning algorithms for computer
vision and natural language processing made these models attractive to other research …

Surveillance video anomaly detection (SVAD) is a challenging task due to the variations in
object scale, discrimination and unexpected events, the impact of the background, and the …

Action localization networks are often structured as a feature encoder sub-network and a
localization sub-network, where the feature encoder learns to transform an input video to …

Accurate landslide extraction is significant for landslide disaster prevention and control.
Remote sensing images have been widely used in landslide investigation, and landslide …

State of the art (SOTA) few-shot learning (FSL) methods suffer significant performance drop
in the presence of domain differences between source and target datasets. The strong …

Video anomaly detection is a critical research area, driven by the increasing reliance on
surveillance systems to maintain public safety and security. The implementation of …

Introduction: Accurate diagnosis is crucial for brain tumors, given their low survival rates and
high treatment costs. However, traditional methods relying on manual interpretation of …

Video Anomaly detection, aiming to detect the abnormal behaviors in surveillance videos, is
a challenging task since the anomalous events are diversified and complicated in different …