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

Context-Aware Zero-Shot Learning for Object Recognition

ICML 2019 · Detection and segmentation › Open-set and few-shot detection · approach: Zero-shot segmentation and detection · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add context to the visual model and learned priorM(V) · Visual Genome target domain, p_sup=50%MFR: 10.72 -> 8.96Helped
Add context to the visual model and learned priorM(V) · Visual Genome source domain, p_sup=50%MFR: 2.64 -> 1.92Helped
Combine source semantic labels, source visual context, and target visual contextM(V) · Visual Genome target domain, p_sup=50%MFR: 10.72 -> 8.31Helped
Combine source semantic labels, source visual context, and target visual contextM(V) · Visual Genome source domain, p_sup=50%MFR: 2.64 -> 1.79Helped
Model context and visual appearance separately rather than jointly (ablation)M(C_S_H, V) · Visual Genome target domain, p_sup=50%MFR: 10.07 -> 8.96Helped
Use annotated context objects rather than masked whole-image context (ablation)M(C_I, V) · Visual Genome target domain, p_sup=50%MFR: 9.19 -> 8.31Helped
Use context semantic labels instead of source visual context features (ablation)M(C_S_L, V) · Visual Genome target domain, p_sup=50%MFR: 9.01 -> 8.96Helped
Add source visual context features to source semantic labels and target visual context (ablation)M(C_S_H ∪ T_L, V) · Visual Genome target domain, p_sup=50%MFR: 8.52 -> 8.31Helped
Use DeViSE-style uniform negative sampling instead of a separately learned prior (ablation)M(V) · Visual Genome source domain, p_sup=50%MFR: 2.64 -> 3.62Hurt
Add context for classes with broad or unexpected contextsM(V) · Visual Genome target domain, selected examplescorrect-class rankMixed

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