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Found 11 papers, 2 papers with code
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.
Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions
The current study focuses on systematically analyzing the recent advances in the field of Multimodal eXplainable Artificial Intelligence (MXAI).
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.
User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments
Learning from Demonstration (LfD) has been established as the dominant paradigm for efficiently transferring skills from human teachers to robots.
Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning
Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems.
A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition
In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted.
Neural Network Compression Using Higher-Order Statistics and AuxiliaryReconstruction Losses
In this paper, the problem of pruning and compressingthe weights of various layers of deep neural networks is in-vestigated.
Deep Affordance-grounded Sensorimotor Object Recognition
It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them.
