Unsupervised and Supervised Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 2)
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...
| Authors | Thierry Bouwmans, Anastasia Zakharova, Belmar Garcia et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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