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Found 6 papers, 4 papers with code
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection
We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy.
Asynchronous Bioplausible Neuron for Spiking Neural Networks for Event-Based Vision
Spiking Neural Networks (SNNs) offer a biologically inspired approach to computer vision that can lead to more efficient processing of visual data with reduced energy consumption.
Gait Data Augmentation using Physics-Based Biomechanical Simulation
This paper focuses on addressing the problem of data scarcity for gait analysis.
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network
In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability.
Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Grasping
Object segmentation for robotic grasping under dynamic conditions often faces challenges such as occlusion, low light conditions, motion blur and object size variance.
A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environment
To the best of our knowledge, this densely annotated and 3D spatial-temporal event-based segmentation benchmark of tabletop objects is the first of its kind.
