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Found 20 papers, 5 papers with code
Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey
This paper presents a comprehensive survey of pose-based applications utilizing deep learning, encompassing pose estimation, pose tracking, and action recognition. Pose estimation involves the determination of human joint positions from images or image sequences.
Context-Aware Block Net for Small Object Detection
State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects.
Ranked #1 on
Traffic Sign Detection
on TT100K
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).
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.
EDSC: An Event-Driven Smart Contract Platform
This paper presents EDSC, a novel smart contract platform design based on the event-driven execution model as opposed to the traditionally employed transaction-driven execution model.
Distributed, Parallel, and Cluster Computing
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.
Election with Bribed Voter Uncertainty: Hardness and Approximation Algorithm
In this paper, we introduce a novel variant of the bribery problem, "Election with Bribed Voter Uncertainty" or BVU for short, accommodating the uncertainty that the vote of a bribed voter may or may not be counted.
MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects
The performance of small object detection, however, is still less than satisfactory because of the deficiency of semantic information on shallow feature maps.
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.
Dual Long Short-Term Memory Networks for Sub-Character Representation Learning
To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example.
Effective Neural Solution for Multi-Criteria Word Segmentation
We present a simple yet elegant solution to train a single joint model on multi-criteria corpora for Chinese Word Segmentation (CWS).
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
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).
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
HEp-2 Cell Image Classification with Deep Convolutional Neural Networks
Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases.
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.
