Video representation learning by identifying spatio-temporal transformations

2021 ◽  
Author(s):  
Sheng Geng ◽  
Shimin Zhao ◽  
Hu Liu
Keyword(s):  
Representation Learning ◽  
Video Representation ◽  
Spatio Temporal

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

Dynamic-boosting attention for self-supervised video representation learning

2021 ◽  
Author(s):  
Zhipeng Wang ◽  
Chunping Hou ◽  
Guanghui Yue ◽  
Qingyuan Yang
Keyword(s):  
Representation Learning ◽  
Video Representation

Multi-modal transportation recommendation with unified route representation learning

2020 ◽  
Vol 14 (3) ◽  
pp. 342-350
Author(s):  
Hao Liu ◽  
Jindong Han ◽  
Yanjie Fu ◽  
Jingbo Zhou ◽  
Xinjiang Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
Transportation Networks ◽  
Representation Learning ◽  
Transportation Systems ◽  
Graph Representation ◽  
Dynamic Graph ◽  
Arbitrary Length ◽  
Task Learning ◽  
Semantic Coherence ◽  
Spatio Temporal

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.

Video Representation and Retrieval Using Spatio-temporal Descriptors and Region Relations

2006 ◽  
pp. 94-103 ◽  
Author(s):  
Sotirios Chatzis ◽  
Anastasios Doulamis ◽  
Dimitrios Kosmopoulos ◽  
Theodora Varvarigou
Keyword(s):  
Video Representation ◽  
Spatio Temporal

ON THE REPRESENTATION, LEARNING AND TRANSFER OF SPATIO-TEMPORAL MOVEMENT CHARACTERISTICS

2004 ◽  
Vol 01 (04) ◽  
pp. 613-636 ◽  
Author(s):  
WINFRIED ILG ◽  
GÖKHAN H. BAKIR ◽  
JOHANNES MEZGER ◽  
MARTIN A. GIESE
Keyword(s):  
Representation Learning ◽  
Movement Sequences ◽  
Movement Trajectories ◽  
Movement Characteristics ◽  
Human Movements ◽  
Complex Sequences ◽  
Movement Representation ◽  
Spatio Temporal ◽  
Morphable Models ◽  
Complex Movement

In this paper we present a learning-based approach for the modeling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMs) we derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modeling and synthesis of complex sequences of human movements that contain movement elements with a variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics.

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