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Found 4 papers, 4 papers with code
Separable Convolutions for Optimizing 3D Stereo Networks
In this work first, we show that these 3D convolutions in stereo networks consume up to 94% of overall network operations and act as a major bottleneck.
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
Depending on the dimension of cost volume, we design a 2D and a 3D model with encoder-decoders built from 2D and 3D convolutions, respectively.
Ranked #1 on
Stereo Depth Estimation
on sceneflow
Improving Super-Resolution Methods via Incremental Residual Learning
Recently, Convolutional Neural Networks (CNNs) have shown promising performance in super-resolution (SR).
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
All the existing image steganography methods use manually crafted features to hide binary payloads into cover images.
