Search Results for author:
Found 9 papers, 4 papers with code
Three-way causal attribute partial order structure analysis
In this paper, we propose the method named three-way causal attribute partial order structure (3WCAPOS) to evolve the POFSA from set coverage to causal coverage in order to increase the interpretability and classification performance of the model.
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling
To better model the diversity of interactions, STGNN introduces a novel aggregation mechanism to organize the most significant historical neighbors' information and adaptively obtain the significance of node pairs.
Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning
It is convenient to treat PCL as a standard training strategy and apply it to many other works in self-supervised video feature learning.
Ranked #10 on
Self-supervised Video Retrieval
on UCF101
Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn video representations.
Ranked #10 on
Self-supervised Video Retrieval
on HMDB51
Motion Representation Using Residual Frames with 3D CNN
In this paper, we propose a fast but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets.
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition
Further analysis indicates that better motion features can be extracted using residual frames with 3D ConvNets, and our residual-frame-input path is a good supplement for existing RGB-frame-input models.
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning
For each subtask, the manager is trained to set a subgoal only by a task-level binary label, which requires much fewer labels than conventional approaches.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Music Sequence Prediction with Mixture Hidden Markov Models
Recommendation systems that automatically generate personalized music playlists for users have attracted tremendous attention in recent years.
PERS: A Personalized and Explainable POI Recommender System
The major contributions of this paper are: (i) it models users and locations based on the aspects posted by user via reviews, (ii) it exploits a deep neural network to model the review-aspect category correlation, (iii) it provisions the incorporation of multiple contexts (e. g., categorical, spatial, etc.)
