599 citations · 954 across the 6 of their papers we have counts for
6 papers
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors
Akhmedkhan Shabanov, Ilya Krotov, Nikolay Chinaev +7
Consumer-level depth cameras and depth sensors embedded in mobile devices enable numerous applications, such as AR games and face identification. However, the quality of the captur…
Impostor Networks for Fast Fine-Grained Recognition
Vadim Lebedev, Artem Babenko, Victor Lempitsky
In this work we introduce impostor networks, an architecture that allows to perform fine-grained recognition with high accuracy and using a light-weight convolutional network, maki…
Learnable Visual Markers
Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky
We propose a new approach to designing visual markers (analogous to QR-codes, markers for augmented reality, and robotic fiducial tags) based on the advances in deep generative net…
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi +1
Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and…
Fast ConvNets Using Group-wise Brain Damage
Vadim Lebedev, Victor Lempitsky
We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers…
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba +2
We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning. Gi…