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Accurate Image Super-Resolution Using Very Deep Convolutional Networks
TLDR
This work presents a highly accurate single-image superresolution (SR) method using a very deep convolutional network inspired by VGG-net used for ImageNet classification and uses extremely high learning rates enabled by adjustable gradient clipping.
Enhanced Deep Residual Networks for Single Image Super-Resolution
TLDR
This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
TLDR
This work proposes a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources and presents a new large-scale dataset that provides pairs of realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera.
Deeply-Recursive Convolutional Network for Image Super-Resolution
TLDR
This work proposes an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN) with two extensions: recursive-supervision and skip-connection, which outperforms previous methods by a large margin.
Visual tracking decomposition
We propose a novel tracking algorithm that can work robustly in a challenging scenario such that several kinds of appearance and motion changes of an object occur at the same time. Our algorithm is
Reweighted Random Walks for Graph Matching
TLDR
This work proposes a robust graph matching algorithm against outliers and deformation by simulating random walks with reweighting jumps enforcing the matching constraints on the association graph and achieves noise-robust graph matching by iteratively updating and exploiting the confidences of candidate correspondences.
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
TLDR
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
TLDR
This model is designed as a 3D CNN that provides accurate estimates while running in real-time and outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based3D hand pose estimation challenge.
Tracking by Sampling Trackers
TLDR
A novel tracking framework called visual tracker sampler that tracks a target robustly by searching for the appropriate trackers in each frame by using the Markov Chain Monte Carlo method from the predefined tracker space.
Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation From a Single RGB Image
TLDR
This work firstly proposes a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image, which achieves comparable results with the state-of-the-art 3D single- person pose estimation models without any groundtruth information and significantly outperforms previous 3DMulti-Person pose estimation methods on publicly available datasets.
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