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Self-supervised visual learning in the low-data regime: a comparative evaluation
Self-Supervised Learning (SSL) is a valuable and robust training methodology for contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a `pretext task' that does not require ground-truth labels/annotation.
Illicit object detection in X-ray images using Vision Transformers
The results demonstrate the remarkable accuracy of the DINO Transformer detector in the low-data regime, the impressive real-time performance of YOLOv8, and the effectiveness of the hybrid NextViT backbone.
Self-supervised visual learning for analyzing firearms trafficking activities on the Web
Automated visual firearms classification from RGB images is an important real-world task with applications in public space security, intelligence gathering and law enforcement investigations.
Visual inspection for illicit items in X-ray images using Deep Learning
Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc.
Neural Natural Language Processing for Long Texts: A Survey on Classification and Summarization
Secondly, it offers a brief examination of the current state-of-the-art in two key long document analysis tasks: document classification and document summarization.
Illicit item detection in X-ray images for security applications
This highlights the potential of Big Data analysis in enhancing public safety.
