Future prediction
38 papers with code • 0 benchmarks • 1 datasets
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Most implemented papers
Probabilistic Video Generation using Holistic Attribute Control
Videos express highly structured spatio-temporal patterns of visual data.
Deep RNN Framework for Visual Sequential Applications
There are mainly two novel designs in our deep RNN framework: one is a new RNN module called Context Bridge Module (CBM) which splits the information flowing along the sequence (temporal direction) and along depth (spatial representation direction), making it easier to train when building deep by balancing these two directions; the other is the Overlap Coherence Training Scheme that reduces the training complexity for long visual sequential tasks on account of the limitation of computing resources.
INFER: INtermediate representations for FuturE pRediction
Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving).
Learning Representations for Predicting Future Activities
Foreseeing the future is one of the key factors of intelligence.
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers.
Predicting 3D Human Dynamics from Video
In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input.
PiP: Planning-informed Trajectory Prediction for Autonomous Driving
Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.
Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction
To filter out irrelevant noises and alleviate the error propagation, DSAN dynamically extracts valuable information by applying self-attention over the noisy input and bridges each output directly to the purified inputs via implementing a switch-attention mechanism.
Unsupervised Learning of Video Representations via Dense Trajectory Clustering
This paper addresses the task of unsupervised learning of representations for action recognition in videos.
Data-Efficient Reinforcement Learning with Self-Predictive Representations
We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.