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computer-vision-4

Future prediction

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

Philip H. S. Torr, Manmohan Chandraker, Namhoon Lee, Wongun Choi, Paul Vernaza, C. Choy•Thu Apr 13 2017

The proposed Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes significantly improves the prediction accuracy compared to other baseline methods.

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

Lu Jiang, Li Fei-Fei, Juan Carlos Niebles, A. Hauptmann, Junwei Liang•Sun Feb 10 2019

An end-to-end, multi-task learning system utilizing rich visual features about human behavioral information and interaction with their surroundings is proposed, providing the first empirical evidence that joint modeling of paths and activities benefits future path prediction.

412 0

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Compositional Video Prediction

A. Gupta, Shubham Tulsiani, Yufei Ye, Maneesh Singh•Wed Aug 21 2019

An approach for pixel-level future prediction given an input image of a scene observing that a scene is comprised of distinct entities that undergo motion is presented and empirically validate the approach against alternate representations and ways of incorporating multi-modality.

84 0

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Temporal Aggregate Representations for Long-Range Video Understanding

Fadime Sener, Angela Yao, Dipika Singhania•Sun May 31 2020

This work addresses questions of temporal extent, scaling, and level of semantic abstraction with a flexible multi-granular temporal aggregation framework and shows that it is possible to achieve state of the art in both next action and dense anticipation with simple techniques such as max-pooling and attention.

169 0

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

Wentao Bao, Qi Yu, Yu Kong•Fri Jul 31 2020

An uncertainty-based accident anticipation model with spatio-temporal relational learning that sequentially predicts the probability of traffic accident occurrence with dashcam videos is proposed to take advantage of graph convolution and recurrent networks for relational feature learning, and leverage Bayesian neural networks to address the intrinsic variability of latent relational representations.

192 0

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MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction

Drago Anguelov, Benjamin Sapp, Balakrishnan Varadarajan, Ahmed S. Hefny, A. Srivastava, Khaled S. Refaat, Nigamaa Nayakanti, Andre Cornman, K. Chen, B. Douillard, C. Lam•Sun Nov 28 2021

This paper presents MultiPath++, a future prediction model that achieves state-of-the-art performance on popular benchmarks, and reconsiders the choice of pre-defined static anchors, and develops a way to learn latent anchor embeddings end-to-end in the model.

378 0

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TrajPRed: Trajectory Prediction With Region-Based Relation Learning

Chen Zhou, Ghassan AlRegib, Armin Parchami, Kunjan Singh•Tue Apr 09 2024

A region-based relation learning paradigm that models social interactions via region-wise dynamics of joint states, i.e., the changes in the density of crowds, and it is shown that diverse prediction benefits from region-based relation learning.

7 0

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Interpreting tree ensembles with inTrees

Houtao Deng•Thu Aug 21 2014

This work provides the interpretable trees (inTrees) framework that extracts, measures, prunes, selects, and summarizes rules from a tree ensemble, and calculates frequent variable interactions.

260 0

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Decomposing Motion and Content for Natural Video Sequence Prediction

Honglak Lee, Seunghoon Hong, Jimei Yang, Ruben Villegas, Xunyu Lin•Sat Jun 24 2017

To the best of the 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.

630 0

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Future prediction | State-of-the-Art