Unsupervised and Supervised Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 2)

arXiv:2610.18291v1 Announce Type: new Abstract: Since the rise of supervised deep learning modelet dets, researchers has attempt to solve infrared small targection using various models like conventional neural networks, GANS, Transformers, Mixture of Experts and recent graph/hypergraph neural networks. Moreover, these deep neural networks methods surpassed the traditional unsupervised models and the low-rank and sparse decomposition unsupervised models reviewed in "Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1). In this context, the aim of this paper is to provide a complete survey of the deep learning models in infrared small target detection. First, we categorize the different approaches found in the literature in terms of the modeling models of strategies. Then, we have discussed them in terms of the critical situations that they claim to handle. Furthermore, we present the specific performance evaluation. Then, we conclude with several promising directions for future research.

Related

• Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)
• Exploring the Limits of End-to-End Feature-Affinity Propagation for Single-Point Supervised Infrared Small Target Detection
• Diffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection
• Breaking Self-Attention Failure: Rethinking Query Initialization for Infrared Small Target Detection
• Understand Before Detect: Vision--Language Learning for Omni-Domain Infrared Small Target Detection

Source: arXiv cs.CV | 2026-10-14