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Learning the Redundancy-Free Features for Generalized Zero-Shot Object Recognition

CVPR 2020 · Detection and segmentation › Open-set and few-shot detection · approach: Zero-shot segmentation and detection · 113 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use redundancy-free feature generation instead of GMNGZSL feature generation · AWAH: 65.8 -> 66.5Helped
Use redundancy-free feature generation instead of SABRGZSL feature generation · CUBH: 56.8 -> 54.6Hurt
Use redundancy-free feature generation instead of SABRGZSL feature generation · SUNH: 41.5 -> 41.9Helped
Use redundancy-free feature generation instead of LisGANGZSL feature generation · FLOH: 68.3 -> 71.1Helped
Add redundancy-free features to semantic embeddingSJE-based GZSL semantic embedding · AWAH: 19.6 -> 34.8Helped
Add redundancy-free features to semantic embeddingSJE-based GZSL semantic embedding · CUBH: 33.6 -> 36.9Helped
Add redundancy-free features to semantic embeddingSJE-based GZSL semantic embedding · SUNH: 19.8 -> 27.3Helped
Add redundancy-free features to semantic embeddingSJE-based GZSL semantic embedding · FLOH: 21.5 -> 36.3Helped
Increase the number of synthetic samples per unseen class (ablation)RFF-GZSL · AWA, CUB, SUN, and FLOU and HHelped
Increase the redundancy-free feature dimension (ablation)RFF-GZSL · AWA, CUB, SUN, and FLOGZSL performanceHelped

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