Search Results for author:
Found 15 papers, 0 papers with code
EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series
Transformer-based models for anomaly detection in multivariate time series can benefit from the self-attention mechanism due to its advantage in modeling long-term dependencies.
Learning Latent Global Network for Skeleton-based Action Prediction
Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes.
Ranked #4 on
Skeleton Based Action Recognition
on SYSU 3D
Automatic Hierarchical Classification of Kelps using Deep Residual Features
Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas.
Semi-supervised Learning on Graph with an Alternating Diffusion Process
Graph-based semi-supervised learning usually involves two separate stages, constructing an affinity graph and then propagating labels for transductive inference on the graph.
Learning clip representations for skeleton-based 3d action recognition
This paper presents a new representation of skeleton sequences for 3D action recognition.
Ranked #60 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Exploiting Layerwise Convexity of Rectifier Networks with Sign Constrained Weights
By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization-minimization (MM) algorithms.
On the Compressive Power of Deep Rectifier Networks for High Resolution Representation of Class Boundaries
To show the superior compressive power of deep rectifier networks over shallow rectifier networks, we prove that the maximum boundary resolution of a single hidden layer rectifier network classifier grows exponentially with the number of units when this number is smaller than the dimension of the patterns.
Skeletonnet: Mining deep part features for 3-d action recognition
Given a skeleton sequence, the spatial structure of the skeleton joints in each frame and the temporal information between multiple frames are two important factors for action recognition.
Ranked #106 on
Skeleton Based Action Recognition
on NTU RGB+D
From Deep to Shallow: Transformations of Deep Rectifier Networks
Similarly, for a residual net and a conventional rectifier net with the same structure except for the skip connections in the residual net, the corresponding single hidden layer representation of the residual net is much more complex than the corresponding single hidden layer representation of the conventional net.
A New Representation of Skeleton Sequences for 3D Action Recognition
This paper presents a new method for 3D action recognition with skeleton sequences (i. e., 3D trajectories of human skeleton joints).
Ranked #65 on
Skeleton Based Action Recognition
on NTU RGB+D 120
ResFeats: Residual Network Based Features for Image Classification
Deep residual networks have recently emerged as the state-of-the-art architecture in image segmentation and object detection.
Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction
The structural models, including the spatial and the temporal models, are learned with Long Short Term Memory (LSTM) networks to capture the dependency of the global and local contexts of each RGB frame and each optical flow image, respectively.
Contractive Rectifier Networks for Nonlinear Maximum Margin Classification
The contractive constraints ensure that the achieved separating margin in the input space is larger than or equal to the separating margin in the output layer.
A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification
This paper introduces a new learnable feature descriptor called "spatial layout and scale invariant convolutional activations" to deal with these challenges.
Learning Non-Linear Reconstruction Models for Image Set Classification
We propose a deep learning framework for image set classification with application to face recognition.
