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Hierarchical recurrent neural network for skeleton based action recognition
  • Yong Du, Wei Wang, Liang Wang
  • Computer Science
    IEEE Conference on Computer Vision and Pattern…
  • 7 June 2015
TLDR
This paper proposes an end-to-end hierarchical RNN for skeleton based action recognition, and demonstrates that the model achieves the state-of-the-art performance with high computational efficiency.
Session-based Recommendation with Graph Neural Networks
TLDR
In the proposed method, session sequences are modeled as graph-structured data and GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods.
A survey on visual surveillance of object motion and behaviors
TLDR
This paper reviews recent developments and general strategies of the processing framework of visual surveillance in dynamic scenes, and analyzes possible research directions, e.g., occlusion handling, a combination of two and three-dimensional tracking, and fusion of information from multiple sensors, and remote surveillance.
Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
TLDR
RNN is extended and a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN) is proposed, which can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transitions for different geographical distances.
Silhouette Analysis-Based Gait Recognition for Human Identification
TLDR
A simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed that implicitly captures the structural and transitional characteristics of gait.
Deep semantic ranking based hashing for multi-label image retrieval
TLDR
In this work, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features.
A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs
TLDR
Experimental results show that this first work based on deep CNNs for gait recognition in the literature outperforms the previous state-of-the-art methods by a significant margin, and shows great potential for practical applications.
See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-identification
TLDR
This paper focuses on video-based person re-identification and builds an end-to-end deep neural network architecture to jointly learn features and metrics and integrates the surrounding information at each location by a spatial recurrent model when measuring the similarity with another pedestrian video.
Deep Graph Contrastive Representation Learning
TLDR
This paper proposes a novel framework for unsupervised graph representation learning by leveraging a contrastive objective at the node level, and generates two graph views by corruption and learns node representations by maximizing the agreement of node representations in these two views.
A Dynamic Recurrent Model for Next Basket Recommendation
TLDR
This work proposes a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN), which not only learns a dynamic representation of a user but also captures global sequential features among baskets.
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