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Research map / Detection and segmentation / Open-set and few-shot detection

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CVPR 2018 · Detection and segmentation › Open-set and few-shot detection · approach: Object-level representation learning · 94 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use iterative predicate and inverse-predicate attention shifts instead of entity-only co-occurrenceSSAS · CLEVRMean IoU (S): 0.691 -> 0.778Helped
Use iterative predicate and inverse-predicate attention shifts instead of entity-only co-occurrenceSSAS · VRDMean IoU (S): 0.347 -> 0.369Helped
Use iterative predicate and inverse-predicate attention shifts instead of entity-only co-occurrenceSSAS · Visual GenomeMean IoU (S): 0.414 -> 0.421Helped
Increase inference from 1 to 2 iterations (ablation)SSAS · CLEVRMean IoU (S): 0.742 -> 0.777Helped
Increase inference from 1 to 2 iterations (ablation)SSAS · VRDMean IoU (S): 0.358 -> 0.365Helped
Increase inference from 1 to 2 iterations (ablation)SSAS · Visual GenomeMean IoU (S): 0.426 -> 0.422Hurt
Increase inference from 2 to 3 iterations (ablation)SSAS · CLEVRMean IoU (S): 0.777 -> 0.778Helped
Increase inference from 2 to 3 iterations (ablation)SSAS · VRDMean IoU (S): 0.365 -> 0.369Helped
Increase inference from 2 to 3 iterations (ablation)SSAS · Visual GenomeMean IoU (S): 0.422 -> 0.421No clear effect
Train with the subject or object randomly masked at drop rate 0.3SSAS · VRD, no subjectS-IoU: 0.208 -> 0.335Helped
Train with entity masking and infer without both entity categoriesSSAS · VRD, only predicateS-IoU: 0.024 -> 0.334Helped

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