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Found 9 papers, 0 papers with code
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods
Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences.
Self-supervised Modal and View Invariant Feature Learning
By exploring the inherent multi-modality attributes of 3D objects, in this paper, we propose to jointly learn modal-invariant and view-invariant features from different modalities including image, point cloud, and mesh with heterogeneous networks for 3D data.
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network.
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization
This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs).
Coarse-to-fine Semantic Segmentation from Image-level Labels
The enhanced coarse mask is fed to a fully convolutional neural network to be recursively refined.
Skeleton Boxes: Solving skeleton based action detection with a single deep convolutional neural network
However, due to the difficulty in representing the 3D skeleton video and the lack of training data, action detection from streaming 3D skeleton video still lags far behind its recognition counterpart and image based object detection.
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn
Especially on the largest and challenge NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods by a large margion, which proves the efficacy of the proposed method.
Ranked #80 on
Skeleton Based Action Recognition
on NTU RGB+D
