LoHGNet combines Lorentz geometric encoding with high-order hypergraph relation learning to achieve competitive accuracy in infrared small target detection on complex backgrounds.
Infrared small target detection based on the weighted strengthened local contrast measure
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
baseline 1polarities
baseline 1representative citing papers
Na-IRSTD improves infrared small target detection by fusing native-resolution features with a selective token reduction strategy, reaching state-of-the-art results on four public benchmarks.
citing papers explorer
-
LoHGNet: Infrared Small Target Detection through Lorentz Geometric Encoding with High-Order Relation Learning
LoHGNet combines Lorentz geometric encoding with high-order hypergraph relation learning to achieve competitive accuracy in infrared small target detection on complex backgrounds.
-
Na-IRSTD: Enhancing Infrared Small Target Detection via Native-Resolution Feature Selection and Fusion
Na-IRSTD improves infrared small target detection by fusing native-resolution features with a selective token reduction strategy, reaching state-of-the-art results on four public benchmarks.