Search Results for author:
Found 52 papers, 7 papers with code
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition
To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton based action recognition is first investigated.
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
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition
Besides, these methods directly calculate the pair-wise global self-attention equally for all the joints in both the spatial and temporal dimensions, undervaluing the effect of discriminative local joints and the short-range temporal dynamics.
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.
Deep Stereo Image Compression via Bi-Directional Coding
Existing learning-based stereo compression methods usually adopt a unidirectional approach to encoding one image independently and the other image conditioned upon the first.
Multi-View Stereo with Transformer
It is built upon Transformer and is capable of extracting dense features with global context and 3D consistency, which are crucial to achieving reliable matching for MVS.
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.
Novel View Synthesis from a Single Image via Unsupervised learning
In particular, the network consists of a token transformation module (TTM) that facilities the transformation of the features extracted from a source viewpoint image into an intrinsic representation with respect to a pre-defined reference pose and a view generation module (VGM) that synthesizes an arbitrary view from the representation.
Context-guided Triple Matching for Multiple Choice Question Answering
The task of multiple choice question answering (MCQA) refers to identifying a suitable answer from multiple candidates, by estimating the matching score among the triple of the passage, question and answer.
Regression on Deep Visual Features using Artificial Neural Networks (ANNs) to Predict Hydraulic Blockage at Culverts
Positive value of $R^{2}$ score indicated the presence of correlation between visual features and hydraulic blockage and suggested that both can be interrelated with each other.
Automating Visual Blockage Classification of Culverts with Deep Learning
Blockage of culverts by transported debris materials is reported as main contributor in originating urban flash floods.
Prediction of Hydraulic Blockage at Cross Drainage Structures using Regression Analysis
Hydraulic blockage of cross-drainage structures such as culverts is considered one of main contributor in triggering urban flash floods.
A Two-stream Neural Network for Pose-based Hand Gesture Recognition
The self-attention based graph convolutional network has a dynamic self-attention mechanism to adaptively exploit the relationships of all hand joints in addition to the fixed topology and local feature extraction in the GCN.
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.
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition
Smartphone sensors based human activity recognition is attracting increasing interests nowadays with the popularization of smartphones.
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.
Unsupervised Domain Expansion from Multiple Sources
Given an existing system learned from previous source domains, it is desirable to adapt the system to new domains without accessing and forgetting all the previous domains in some applications.
Deep Independently Recurrent Neural Network (IndRNN)
Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient vanishing and exploding problems and thus difficult to learn long-term patterns and construct deep networks.
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects.
Importance Weighted Adversarial Nets for Partial Domain Adaptation
This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain.
Unsupervised Domain Adaptation: A Multi-task Learning-based Method
This paper presents a novel multi-task learning-based method for unsupervised domain adaptation.
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI), for both isolated and continuous action recognition.
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly.
Ranked #10 on
Language Modelling
on Penn Treebank (Character Level)
Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition
Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities.
RGB-D-based Human Motion Recognition with Deep Learning: A Survey
Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data.
Foreground Detection in Camouflaged Scenes
This paper proposes a texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes.
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.
A Fully Trainable Network with RNN-based Pooling
Such a network with learnable pooling function is referred to as a fully trainable network (FTN).
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition.
Ranked #5 on
Domain Adaptation
on Office-Caltech
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition.
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis.
Ranked #105 on
Skeleton Based Action Recognition
on NTU RGB+D
(Accuracy (CV) metric)
Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks
Based on the scene flow vectors, we propose a new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition.
Ranked #3 on
Hand Gesture Recognition
on ChaLearn val
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI).
Ranked #2 on
Hand Gesture Recognition
on ChaLearn val
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.
Exploiting Structure Sparsity for Covariance-based Visual Representation
A variety of methods have been proposed to boost its efficacy, with some recent ones resorting to nonlinear kernel technique.
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets).
Learning a Pose Lexicon for Semantic Action Recognition
This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features.
Creating Simplified 3D Models with High Quality Textures
This paper presents an extension to the KinectFusion algorithm which allows creating simplified 3D models with high quality RGB textures.
Planogram Compliance Checking Based on Detection of Recurring Patterns
Product layout is extracted from an input image by means of unsupervised recurring pattern detection and matched via graph matching with the expected product layout specified by a planogram to measure the level of compliance.
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.
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010.
Beyond Covariance: Feature Representation With Nonlinear Kernel Matrices
It proposes an open framework to use the kernel matrix over feature dimensions as a generic representation and discusses its properties and advantages.
Online Action Recognition based on Incremental Learning of Weighted Covariance Descriptors
This paper presents a fast yet effective method to recognize actions from stream of noisy skeleton data, and a novel weighted covariance descriptor is adopted to accumulate evidence.
Action recognition from depth maps using deep convolutional neural networks
In addition, the method was evaluated on the large dataset constructed from the above datasets.
Ranked #9 on
Multimodal Activity Recognition
on EV-Action
Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences
The results show that our approach can achieve state-of-the-art results on the individual datasets and without dramatical performance degradation on the Combined Dataset.
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
These parts are referred to as Frequent Local Parts or FLPs.
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
A comprehensive experimental study is conducted on a variety of image classification tasks to compare our proposed discriminative Stein kernel with the original Stein kernel and other commonly used methods for evaluating the similarity between SPD matrices.
A Fast Approximate AIB Algorithm for Distributional Word Clustering
Distributional word clustering merges the words having similar probability distributions to attain reliable parameter estimation, compact classification models and even better classification performance.
