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Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks.
TLDR
In this paper, a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people, and predicts socially plausible future by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss.Abstract:
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in the future. We tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people. We predict socially plausible futures by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss. Through experiments on several datasets we demonstrate that our approach outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.read more
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SimAug: Learning Robust Representations from Simulation for Trajectory Prediction.
TL;DR: A novel approach to learn robust representation through augmenting the simulation training data such that the representation can better generalize to unseen real-world test data.
Journal ArticleDOI
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
Irtiza Hasan,Francesco Setti,Theodore Tsesmelis,Vasileios Belagiannis,Sikandar Amin,Alessio Del Bue,Marco Cristani,Fabio Galasso +7 more
TL;DR: In this paper, a joint optimization of full covariance matrices during the LSTM backpropagation was proposed to predict future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting.
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Conditional Flow Variational Autoencoders for Structured Sequence Prediction
Apratim Bhattacharyya,Michael Hanselmann,Mario Fritz,Bernt Schiele,Christoph-Nikolas Straehle +4 more
TL;DR: This work introduces Conditional Flow Variational Autoencoders (CF-VAE) and proposes two novel regularization schemes which stabilizes training and deals with posterior collapse for stable training and better fit to the target data distribution.
Proceedings ArticleDOI
Recursive Social Behavior Graph for Trajectory Prediction
TL;DR: Wang et al. as mentioned in this paper used graph convolutional neural network (CNN) to propagate social interaction information in a social behavior graph and achieved state-of-the-art performance on the ETH and UCY datasets.
Journal ArticleDOI
Single Image Snow Removal via Composition Generative Adversarial Networks
TL;DR: This paper proposes a composition generative adversarial network for removing snowflakes from a single image and shows that the network has a good effect and it is superior to the other state-of-the-art methods.
References
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Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
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Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.