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Found 5 papers, 1 papers with code
Low-Cost and Real-Time Industrial Human Action Recognitions Based on Large-Scale Foundation Models
Industrial managements, including quality control, cost and safety optimization, etc., heavily rely on high quality industrial human action recognitions (IHARs) which were hard to be implemented in large-scale industrial scenes due to their high costs and poor real-time performance.
Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification
Remarkable successes were made in Medical Image Classification (MIC) recently, mainly due to wide applications of convolutional neural networks (CNNs).
Transcending Adversarial Perturbations: Manifold-Aided Adversarial Examples with Legitimate Semantics
Deep neural networks were significantly vulnerable to adversarial examples manipulated by malicious tiny perturbations.
Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning
However, the existing partitioning rules either have no constraints on the topology of regions or require the same topology for all regions.
DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes
Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images.
