Future prediction

29 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

yadrimz/DESIRE CVPR 2017

DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.

Peeking into the Future: Predicting Future Person Activities and Locations in Videos

google/next-prediction CVPR 2019

To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.

Compositional Video Prediction

JudyYe/CVP ICCV 2019

We present an approach for pixel-level future prediction given an input image of a scene.

Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning

Cogito2012/UString 1 Aug 2020

The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features.

Interpreting Tree Ensembles with inTrees

IBCNServices/GENESIM 23 Aug 2014

Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy.

Decomposing Motion and Content for Natural Video Sequence Prediction

rubenvillegas/iclr2017mcnet 25 Jun 2017

To the best of our knowledge, this is the first end-to-end trainable network architecture with motion and content separation to model the spatiotemporal dynamics for pixel-level future prediction in natural videos.

Improving Video Generation for Multi-functional Applications

bernhard2202/improved-video-gan 30 Nov 2017

In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.

Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression

yuriautsumi/PersonalizedGP 1 Dec 2017

In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits.

Deep RNN Framework for Visual Sequential Applications

BoPang1996/Deep-RNN-Framework CVPR 2019

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.