3.7k citations
- Meta (United States)US158 papers
- New York UniversityUS11 papers
- Carnegie Mellon UniversityUS10 papers
- University of California, BerkeleyUS8 papers
- Stanford UniversityUS7 papers
- University of WashingtonUS7 papers
- Cornell UniversityUS6 papers
- University of OxfordGB6 papers
- Massachusetts Institute of TechnologyUS5 papers
- Meta (Israel)IL5 papers
- ETH ZurichCH4 papers
- Google (United States)US4 papers
8 papers · 2 filters
Lip-reading with Densely Connected Temporal Convolutional Networks
Pingchuan Ma, Yujiang Wang, Jie Shen +2
In this work, we present the Densely Connected Temporal Convolutional Network (DC-TCN) for lip-reading of isolated words. Although Temporal Convolutional Networks (TCN) have recent…
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
David Novotny, Roman Shapovalov, Andrea Vedaldi
We propose the Canonical 3D Deformer Map, a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects…
Neural Sparse Voxel Fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin +2
Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is challenging, because it requires the difficult step of capturing detai…
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss
Yurun Tian, Axel Barroso-Laguna, Tony Ng +2
Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature. In this paper, we…
Progressive Skeletonization: Trimming more fat from a network at initialization
Pau de Jorge, Amartya Sanyal, Harkirat S. Behl +3
Recent studies have shown that skeletonization (pruning parameters) of networks \textit{at initialization} provides all the practical benefits of sparsity both at inference and tra…
Consistent Video Depth Estimation
Xuan Luo, Jia-Bin Huang, Richard Szeliski +2
We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video. We leverage a conventional structure-from-motion reconstructio…