STAR-IOD applies scale-decoupled topology alignment and K-Means-based pseudo-label refinement to reduce catastrophic forgetting in remote sensing incremental object detection, reporting 1.7% and 2.1% mAP gains on new DIOR-IOD and DOTA-IOD datasets.
arXiv preprint arXiv:2312.16202 , year=
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HiSem adds bidirectional differential attention and a two-level hierarchical routing module with MoE to handle semantic granularity differences in remote sensing change captioning, reporting +7.52% BLEU-4 on WHU-CDC.
UrbanCDNet, a custom Siamese CNN, raises F1 to 0.7511 and IoU to 0.6014 on a Korean urban change-detection benchmark, with largest gains on sparse-change and high-photometric-gap subsets.
citing papers explorer
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STAR-IOD: Scale-decoupled Topology Alignment with Pseudo-label Refinement for Remote Sensing Incremental Object Detection
STAR-IOD applies scale-decoupled topology alignment and K-Means-based pseudo-label refinement to reduce catastrophic forgetting in remote sensing incremental object detection, reporting 1.7% and 2.1% mAP gains on new DIOR-IOD and DOTA-IOD datasets.
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HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning
HiSem adds bidirectional differential attention and a two-level hierarchical routing module with MoE to handle semantic granularity differences in remote sensing change captioning, reporting +7.52% BLEU-4 on WHU-CDC.
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UrbanCDNet: Appearance-Robust and Boundary-Aware Bitemporal Change Detection for Korean Urban Building Monitoring
UrbanCDNet, a custom Siamese CNN, raises F1 to 0.7511 and IoU to 0.6014 on a Korean urban change-detection benchmark, with largest gains on sparse-change and high-photometric-gap subsets.