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GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection

CVPR 2022 · Detection and segmentation › Open-set and few-shot detection · approach: Human-object interaction detection · 155 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use GEN-VLKT_l rather than QPIC-R101HICO-Det test set · HICO-Det Default FullmAP: 29.90 -> 34.95Helped
Use GEN-VLKT_l rather than QPIC-R50V-COCO · Scenario 1role AP: 58.8 -> 63.58Helped
Use GEN-VLKT_s rather than the no-VLKT baselineHICO-Det · RF-UC unseen categoriesmAP: 12.52 -> 21.36Helped
Replace the verb classifier with an HOI triplet classifier (ablation)GEN · HICO-Det default FullmAP: 31.88 -> 30.96Hurt
Add p-GE with independent human and object queries (ablation)GEN Base-verb · HICO-Det default FullmAP: 31.23 -> 31.88Helped
Add i-GE to guide interaction queries (ablation)GEN Base-verb · HICO-Det default FullmAP: 30.83 -> 31.88Helped
Initialize the interaction classifier with CLIP text embeddings (ablation)Base-triplet · HICO-Det default RaremAP: 22.28 -> 26.08Helped
Add CLIP text initialization for the object classifier (ablation)Base-triplet with interaction text · HICO-Det default FullmAP: 31.71 -> 32.09Helped
Add CLIP visual embedding mimic loss (ablation)Base-triplet with text-driven classifiers · HICO-Det default FullmAP: 32.09 -> 33.75Helped
Use L2 rather than L1 alone for visual mimic (ablation)GEN-VLKT · HICO-Det default FullmAP: 33.75 -> 32.41Hurt
Combine L1 and L2 mimic losses rather than using L1 alone (ablation)GEN-VLKT · HICO-Det default FullmAP: 33.75 -> 33.10Hurt

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