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Found 6 papers, 2 papers with code
Lateralization in Agents' Decision Making: Evidence of Benefits/Costs from Artificial Intelligence
Given that lateralization is ubiquitous, it likely has advantages that can benefit artificial intelligence.
Lateralized Learning for Multi-Class Visual Classification Tasks
Hence these systems are not robust against noisy, irrelevant, and redundant data, resulting in the wrong prediction being made with high confidence.
Tracking Passengers and Baggage Items using Multiple Overhead Cameras at Security Checkpoints
Our SSL approach improves object detection by employing a test-time data augmentation and a regression-based, rotation-invariant pseudo-label refinement technique.
Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species
We employ a data augmentation and refinement approach to improve the accuracy of the model predictions.
Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints
The multiple hypothesis tracking algorithm then keeps track of the temporal identifiers of the targets based on the cluster centroids.
Unsupervised Spatio-temporal Latent Feature Clustering for Multiple-object Tracking and Segmentation
The feature vectors and the constraints graphs are then provided to the kmeans clustering algorithm to separate the corresponding data points in the latent space.
