LoHGNet combines Lorentz geometric encoding with high-order hypergraph relation learning to achieve competitive accuracy in infrared small target detection on complex backgrounds.
Attention-guided pyramid context networks for detecting infrared small target under complex background
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3roles
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.
FeedbackSTS-Det improves moving infrared small target detection accuracy and reduces false alarms via a closed-loop spatio-temporal semantic feedback strategy and an embedded sparse semantic module that captures long-range dependencies with low overhead.
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.
-
FeedbackSTS-Det: Sparse Frames-Based Spatio-Temporal Semantic Feedback Network for Moving Infrared Small Target Detection
FeedbackSTS-Det improves moving infrared small target detection accuracy and reduces false alarms via a closed-loop spatio-temporal semantic feedback strategy and an embedded sparse semantic module that captures long-range dependencies with low overhead.