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 partial sum of the tensor nuclear norm
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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.
SANet augments U-Net with a Dual-path Semantic-aware Module using pinwheel convolutions and CBAM, plus a Selective Attention Fusion Module for adaptive cross-scale feature fusion, to improve detection of sub-pixel infrared targets.
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
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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.
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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.
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Selective Attention-Based Network for Robust Infrared Small Target Detection
SANet augments U-Net with a Dual-path Semantic-aware Module using pinwheel convolutions and CBAM, plus a Selective Attention Fusion Module for adaptive cross-scale feature fusion, to improve detection of sub-pixel infrared targets.
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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.