From Semantics to Pixels: Coarse-to-Fine Masked Autoencoders for Hierarchical Visual Understanding | ScienceToStartup | ScienceToStartup

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References (63)

[1]

Wavelet-Driven Masked Image Modeling: A Path to Efficient Visual Representation

2025Wenzhao Xiang, Chang Liu et al.

[2]

AEMIM: Adversarial Examples Meet Masked Image Modeling

2024Wenzhao Xiang, Chang Liu et al.

[3]

H-ViT: A Hierarchical Vision Transformer for Deformable Image Registration

Founder's Pitch

"C2FMAE is a novel coarse-to-fine masked autoencoder that enhances visual representation learning across multiple data granularities."

Visual Understanding•Score: 4•View PDF ↗

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From Semantics to Pixels: Coarse-to-Fine Masked Autoencoders for Hierarchical Visual Understanding

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References (63)

Founder's Pitch

"C2FMAE is a novel coarse-to-fine masked autoencoder that enhances visual representation learning across multiple data granularities."

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From Semantics to Pixels: Coarse-to-Fine Masked Autoencoders for Hierarchical Visual Understanding

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References (63)

Founder's Pitch

"C2FMAE is a novel coarse-to-fine masked autoencoder that enhances visual representation learning across multiple data granularities."

Commercial Viability Breakdown

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