CL-CLIP uses CLIP image-text cost volumes to create class-specific pathways processed by a multi-expert RoI head, improving continual object detection on VOC and COCO over the F-ViT baseline.
arXiv preprint arXiv:2502.05540 , year=
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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.
citing papers explorer
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CL-CLIP: CLIP-Based Continual Learning Framework with Cost-Volume Category Decoupling for Object Detection
CL-CLIP uses CLIP image-text cost volumes to create class-specific pathways processed by a multi-expert RoI head, improving continual object detection on VOC and COCO over the F-ViT baseline.
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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.