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Detail-Revealing Deep Video Super-Resolution
TLDR
This paper shows that proper frame alignment and motion compensation is crucial for achieving high quality results, and proposes a “sub-pixel motion compensation” (SPMC) layer in a CNN framework that can generate visually and quantitatively high-quality results, superior to current state-of-the-arts, without the need of parameter tuning. Expand
UPSNet: A Unified Panoptic Segmentation Network
TLDR
A parameter-free panoptic head is introduced which solves thepanoptic segmentation via pixel-wise classification and first leverages the logits from the previous two heads and then innovatively expands the representation for enabling prediction of an extra unknown class which helps better resolving the conflicts between semantic and instance segmentation. Expand
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation
TLDR
The proposed Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image achieves top performance on surface normal estimation and is on par with state-of-the-art depth estimation methods. Expand
Deep Edge-Aware Filters
TLDR
This work makes the attempt to learn a big and important family of edge-aware operators from data based on a deep convolutional neural network with a gradient domain training procedure, which gives rise to a powerful tool to approximate various filters without knowing the original models and implementation details. Expand
Video Super-Resolution via Deep Draft-Ensemble Learning
TLDR
This work proposes a new direction for fast video super-resolution via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution, and combines SR drafts through the nonlinear process in a deep convolutional neural network (CNN). Expand
Handling motion blur in multi-frame super-resolution
TLDR
The method proposed in this paper tackles the issue of ubiquitous motion blur easily fails multi-frame super-resolution by optimally searching least blurred pixels in MFSR and produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences. Expand
Learning Deep Structured Active Contours End-to-End
TLDR
This work presents Deep Structured Active Contours (DSAC), a novel framework that integrates priors and constraints into the segmentation process, such as continuous boundaries, smooth edges, and sharp corners, and employs Active Contour Models (ACM), a family of constraint- and prior-based polygonal models. Expand
NerveNet: Learning Structured Policy with Graph Neural Networks
TLDR
NerveNet is proposed to explicitly model the structure of an agent, which naturally takes the form of a graph, and is demonstrated to be significantly more transferable and generalizable than policies learned by other models and are able to transfer even in a zero-shot setting. Expand
3D Graph Neural Networks for RGBD Semantic Segmentation
TLDR
This paper proposes a 3D graph neural network (3DGNN) that builds a k-nearest neighbor graph on top of 3D point cloud that uses back-propagation through time to train the model. Expand
Efficient Graph Generation with Graph Recurrent Attention Networks
TLDR
A new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs), which better captures the auto-regressive conditioning between the already-generated and to-be-generated parts of the graph using Graph Neural Networks (GNNs) with attention. Expand
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