Activity Prediction
24 papers with code • 1 benchmarks • 2 datasets
Predict human activities in videos
Home Action Genome
InfiniteRep
Most implemented papers
Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification
Visualizing graph embeddings annotated with predictions of potentially suicidal individuals shows the integrated model could classify such individuals even if they are positioned far from the support group.
Group Activity Prediction with Sequential Relational Anticipation Model
junwenchen/GAP_SRAM
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ECCV 2020
Our model explicitly anticipates both activity features and positions by two graph auto-encoders, aiming to learn a discriminative group representation for group activity prediction.
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making.
Prescriptive Business Process Monitoring for Recommending Next Best Actions
We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI.
Learning to Abstract and Predict Human Actions
RomeroBarata/hierarchical_action_prediction
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We propose Hierarchical Encoder-Refresher-Anticipator, a multi-level neural machine that can learn the structure of human activities by observing a partial hierarchy of events and roll-out such structure into a future prediction in multiple levels of abstraction.
Meta-HAR: Federated Representation Learning for Human Activity Recognition
However, the effectiveness of federated learning for HAR is affected by the fact that each user has different activity types and even a different signal distribution for the same activity type.
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection
We compare these predictive anomaly detection methods to four classical unsupervised anomaly detection approaches (such as Isolation forest and LOF) in the online setting.
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
yf-li15/DisenHCN
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In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph.
UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions
stergiosbamp/deep-physical-activity-prediction
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To this end, we propose UBIWEAR, an end-to-end framework for intelligent physical activity prediction, with the ultimate goal to empower data-driven goal-setting interventions.
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
ml-jku/clamp
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Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks.
