PTNet is a prototype-guided task-adaptive model that jointly performs change detection and captioning on bi-temporal UAV imagery by modeling structured change semantics, outperforming prior methods on the new UCCD urban construction benchmark and WHU-CDC.
Lightglue: Local feature matching at light speed
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CROSS replaces globally consistent metric maps with a pose-aware topological graph of RGB-D keyframes and maintains a bounded Gaussian-mixture belief over poses via sequential hypothesis testing in SE(3) to achieve change-robust spatial-semantic mapping and navigation.
DINO features combined with many-to-many association and the proposed Harmonic Consensus Maximization enable general visual features to compete with specialized models on out-of-distribution image matching and camera pose estimation.
citing papers explorer
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UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model
PTNet is a prototype-guided task-adaptive model that jointly performs change detection and captioning on bi-temporal UAV imagery by modeling structured change semantics, outperforming prior methods on the new UCCD urban construction benchmark and WHU-CDC.
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Change-Robust Online Spatial-Semantic Topological Mapping
CROSS replaces globally consistent metric maps with a pose-aware topological graph of RGB-D keyframes and maintains a bounded Gaussian-mixture belief over poses via sequential hypothesis testing in SE(3) to achieve change-robust spatial-semantic mapping and navigation.
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Deploy DINO with Many-to-Many Association
DINO features combined with many-to-many association and the proposed Harmonic Consensus Maximization enable general visual features to compete with specialized models on out-of-distribution image matching and camera pose estimation.