Zero-Shot and Few-Shot Object Detection for Emerging Threat Scenarios

Abstract

In today's threat landscape, Zero-shot and Few-shot Object Detection have become key techniques in the recognition of unseen or poorly sampled objects. The techniques alleviate the need for manually annotating large datasets to enable intelligent systems to recognize new objects from few examples or semantic knowledge. This study investigates the use of zero-shot and few-shot learning in new applications like intelligent surveillance systems, cybersecurity, autonomy defense and IoT security. The authors explore recent advances in vision-language models, transformer architectures, and foundation models that enhance detection performance and adaptability in dynamic settings. It also points out some of the main problems such as domain adaptation, computational complexity, and constraints on real-time deployment. The study concludes that zero-shot and few-shot object detection are effective solutions in the future to scaling up the problem of threat detection for AI systems.