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Research map / Vision-language and multimodal / CLIP and zero-shot

Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification

AAAI 2022 · Vision-language and multimodal › CLIP and zero-shot · approach: Attribute-based zero-shot learning · 31 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add VSA, aligning cross-modal latent features with classifier guidance (ablation)Dual-VAE · CUBH: 32.7 -> 54.1Helped
Add VSA, aligning cross-modal latent features with classifier guidance (ablation)Dual-VAE · SUNH: 38.3 -> 43.5Helped
Add VSA, aligning cross-modal latent features with classifier guidance (ablation)Dual-VAE · AwA1H: 52.4 -> 65.7Helped
Add VSA, aligning cross-modal latent features with classifier guidance (ablation)Dual-VAE · AwA2H: 66.1 -> 67.9Helped
Add IEM to enhance information in latent variables (ablation)Dual-VAE · CUBH: 32.7 -> 36.1Helped
Add IEM to enhance information in latent variables (ablation)Dual-VAE · SUNH: 38.3 -> 39.0Helped
Add IEM to enhance information in latent variables (ablation)Dual-VAE · AwA1H: 52.4 -> 65.1Helped
Add IEM to enhance information in latent variables (ablation)Dual-VAE · AwA2H: 66.1 -> 66.6Helped
Add IEM to VSA-VAE (ablation)VSA-VAE · CUBH: 54.1 -> 55.2Helped
Add IEM to VSA-VAE (ablation)VSA-VAE · SUNH: 43.5 -> 43.8Helped
Add IEM to VSA-VAE (ablation)VSA-VAE · AwA1H: 65.7 -> 67.3Helped
Add IEM to VSA-VAE (ablation)VSA-VAE · AwA2H: 67.9 -> 68.7Helped

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