Search Results for author:
Found 13 papers, 2 papers with code
Sub-action Prototype Learning for Point-level Weakly-supervised Temporal Action Localization
Point-level weakly-supervised temporal action localization (PWTAL) aims to localize actions with only a single timestamp annotation for each action instance.
Pseudo Label
Weakly-supervised Temporal Action Localization
+1
Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks
To address this problem, we propose the following: (1) a novel convolutional operator called Faster Fourier Convolution (FasterFC) to replace the two consecutive convolution operations typically used in convolutional neural networks (e. g., U-Net, ResNet).
FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis
It has been studied either using first person vision (FPV) or third person vision (TPV).
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning
The CPM identifies non-self positives in a contextual queue to boost learning.
Learning Using Privileged Information for Zero-Shot Action Recognition
This approach has been challenged by the semantic gap between the visual space and semantic space.
A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition
This paper proposes a new graph convolutional operator called central difference graph convolution (CDGC) for skeleton based action recognition.
Trear: Transformer-based RGB-D Egocentric Action Recognition
In this paper, we propose a \textbf{Tr}ansformer-based RGB-D \textbf{e}gocentric \textbf{a}ction \textbf{r}ecognition framework, called Trear.
Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry
In this paper, we propose a method consisting of two camera pose estimators that deal with the information from pairwise images and a short sequence of images respectively.
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching
The recently developed DARTS (Differentiable Architecture Search) is adopted to search for an effective network architecture that is built upon the two types of cells.
Skeleton-based Action Recognition Using LSTM and CNN
Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation.
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) have recently shown promising performance in many computer vision tasks, especially image-based recognition.
Ranked #1 on
Skeleton Based Action Recognition
on Gaming 3D (G3D)
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition.
Combining ConvNets with Hand-Crafted Features for Action Recognition Based on an HMM-SVM Classifier
This paper proposes a new framework for RGB-D-based action recognition that takes advantages of hand-designed features from skeleton data and deeply learned features from depth maps, and exploits effectively both the local and global temporal information.
