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Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval

AAAI 2020 · Vision-language and multimodal › CLIP and zero-shot · approach: Attribute-based zero-shot learning · 40 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add cross-reconstruction loss (ablation)LCALE · Wikipedia image-textAvg MAP: 0.250 -> 0.362Helped
add Wasserstein cross-alignment loss (ablation)LCALE · Wikipedia image-textAvg MAP: 0.333 -> 0.362Helped
add MMD cross-alignment loss (ablation)LCALE · Wikipedia image-textAvg MAP: 0.347 -> 0.362Helped
add cycle-consistency loss (ablation)LCALE · Wikipedia image-textAvg MAP: 0.339 -> 0.362Helped
add cross-reconstruction loss (ablation)LCALE · SketchyMAP: 0.422 -> 0.476Helped
add Wasserstein cross-alignment loss (ablation)LCALE · SketchyMAP: 0.454 -> 0.476Helped
add MMD cross-alignment loss (ablation)LCALE · SketchyMAP: 0.395 -> 0.476Helped
add cycle-consistency loss (ablation)LCALE · SketchyMAP: 0.458 -> 0.476Helped
replace GAN-based class-embedding space (DADN) with VAE latent embeddingsZS-CMR · Wikipedia image-textAvg MAP: 0.298 -> 0.362Helped
replace SEM-PCYC with LCALEZS image-sketch · SketchyMAP: 0.349 -> 0.476Helped
vary latent embedding dimension (25-250); peak near 60-100 (ablation)LCALE · Wikipedia image-textMAPMixed
binary quantized embeddings instead of real-valued (ablation)LCALE · SketchyMAP: 0.476 -> 0.397Hurt

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