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Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network

AAAI 2020 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Transductive and semantic-dictionary zero-shot · 72 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use learnable (visual+semantic) adjacency matrix instead of fixed semantic one (ablation)GCN-only SketchGCN · Sketchy-ExtendedmAP@200: 0.549 -> 0.556Helped
use learnable adjacency matrix instead of fixed one (ablation)GCN-only SketchGCN · TU-Berlin-ExtendedmAP@200: 0.517 -> 0.508Hurt
use learnable adjacency matrix instead of fixed one (ablation)GCN-only SketchGCN · Sketchy-ExtendedmAP: 0.366 -> 0.367No clear effect
use CVAE instead of MLP as semantic reconstruction network (ablation)SketchGCN · Sketchy-ExtendedmAP@200: 0.555 -> 0.568Helped
use CVAE instead of MLP as semantic reconstruction network (ablation)SketchGCN · TU-Berlin-ExtendedmAP: 0.300 -> 0.324Helped
use CVAE instead of MLP as semantic reconstruction network (ablation)SketchGCN · TU-Berlin-ExtendedmAP@200: 0.506 -> 0.528Helped
add MLP semantic reconstruction network to GCN with fixed A (ablation)GCN only · TU-Berlin-ExtendedmAP@200: 0.517 -> 0.506No clear effect
add CVAE reconstruction network and learnable A to GCN-only basic model (ablation)GCN with fixed A · Sketchy-ExtendedmAP: 0.366 -> 0.382Helped
SketchGCN vs best prior method Doodle2SearchZS-SBIR · Sketchy-ExtendedmAP: 0.369 -> 0.382Helped

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