Work

Synergistic Optimization Mechanism of Brain-Inspired Intelligence and Generative Models in Small Target Detection

Year: 2025

Type: conference-paper

Abstract: In the era of generative artificial intelligence, we attempt to apply brain-inspired intelligence and generative models to small target detection, but currently lack a synergistic optimization mechanism. This paper attempts to enhance the detection performance of small targets by integrating the bionic perception of brain-inspired intelligence with the data augmentation capabilities of generative models. Firstly, we simulate the multi-scale attention mechanism of the biological visual system using a brain-inspired spiking neural network (SNN), enhancing the spatial sensitivity of small target detection. Secondly, we generate high-quality small target samples and optimize local feature representation through Generative Adversarial Networks (GANs), alleviating data scarcity and feature ambiguity. This paper proposes for the first time a solution providing a cross-modal collaborative framework for small target detection, exploring the potential of combining brain-inspired mechanisms with generative models.

Source: International Conference on Generative Artificial Intelligence and Information Security (GAIIS)

DOI: 10.1145/3728725.3728763

Authors: Bin Zhang, Liangshun Wu, Jiang Rong, Shixin Huang, Yuwen Li, Meizhen Luo

Institutions: Xinjiang University of Political Science and Law, Shanghai Jiao Tong University, Shenzhen University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Xinjiang University

OpenAlex ID: W4411015022

FWCI: 2.57

Cites: 9

Cited by: 3

Related to: 10