MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction

• URL: http://arxiv.org/abs/2402.12238v2
• Date: Wed, 15 Jan 2025 11:52:13 GMT
• Title: MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction
• Authors: Jiahe Chen, Jinkun Cao, Dahua Lin, Kris Kitani, Jiangmiao Pang,
• Abstract summary: We propose constructing a mixed Gaussian prior for a normalizing flow model for trajectory prediction.<n>Our method achieves state-of-the-art performance in the evaluation of both trajectory alignment and diversity on the popular UCY/ETH and SDD datasets.
• Score: 72.70572835589158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To predict future trajectories, the normalizing flow with a standard Gaussian prior suffers from weak diversity. The ineffectiveness comes from the conflict between the fact of asymmetric and multi-modal distribution of likely outcomes and symmetric and single-modal original distribution and supervision losses. Instead, we propose constructing a mixed Gaussian prior for a normalizing flow model for trajectory prediction. The prior is constructed by analyzing the trajectory patterns in the training samples without requiring extra annotations while showing better expressiveness and being multi-modal and asymmetric. Besides diversity, it also provides better controllability for probabilistic trajectory generation. We name our method Mixed Gaussian Flow (MGF). It achieves state-of-the-art performance in the evaluation of both trajectory alignment and diversity on the popular UCY/ETH and SDD datasets. Code is available at https://github.com/mulplue/MGF.

Related papers

• Gaussian Mixture Flow Matching Models [51.976452482535954]
  Diffusion models approximate the denoising distribution as a Gaussian and predict its mean, whereas flow matching models re parameterize the Gaussian mean as flow velocity. They underperform in few-step sampling due to discretization error and tend to produce over-saturated colors under classifier-free guidance (CFG) We introduce a novel probabilistic guidance scheme that mitigates the over-saturation issues of CFG and improves image generation quality.
  arXiv  Detail & Related papers  (2025-04-07T17:59:42Z)
• Sequential Function-Space Variational Inference via Gaussian Mixture Approximation [0.6827423171182154]
  Sequential function-space variational inference (SFSVI) is a continual learning method based on variational inference. We propose an SFSVI method which uses a Gaussian mixture variational distribution. We find that in terms of final average accuracy, Gaussian mixture methods perform better than Gaussian methods and likelihood-focused methods perform better than prior-focused methods.
  arXiv  Detail & Related papers  (2025-03-10T09:38:35Z)
• End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler [15.372235873766812]
  Learnable mixture priors offer improved control over exploration, adaptability to target support, and increased to counteract mode collapse. Our experimental results demonstrate significant performance improvements across a diverse range of real-world and synthetic benchmark problems.
  arXiv  Detail & Related papers  (2025-03-01T14:58:14Z)
• Stochastic Sampling from Deterministic Flow Models [8.849981177332594]
  We present a method to turn flow models into a family of differential equations (SDEs) that have the same marginal distributions. We empirically demonstrate advantages of our method on a toy Gaussian setup and on the large scale ImageNet generation task.
  arXiv  Detail & Related papers  (2024-10-03T05:18:28Z)
• Flow Map Matching [15.520853806024943]
  Flow map matching is an algorithm that learns the two-time flow map of an underlying ordinary differential equation. We show that flow map matching leads to high-quality samples with significantly reduced sampling cost compared to diffusion or interpolant methods.
  arXiv  Detail & Related papers  (2024-06-11T17:41:26Z)
• Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting [4.714246221974192]
  We develop a novel probabilistic irregular time series forecasting model, Marginalization Consistent Mixtures of Separable Flows (moses) moses outperforms other state-of-the-art marginalization consistent models, performs on par with ProFITi, but different from ProFITi, guarantee marginalization consistency.
  arXiv  Detail & Related papers  (2024-06-11T13:28:43Z)
• Amortizing intractable inference in diffusion models for vision, language, and control [89.65631572949702]
  This paper studies amortized sampling of the posterior over data, $mathbfxsim prm post(mathbfx)propto p(mathbfx)r(mathbfx)$, in a model that consists of a diffusion generative model prior $p(mathbfx)$ and a black-box constraint or function $r(mathbfx)$. We prove the correctness of a data-free learning objective, relative trajectory balance, for training a diffusion model that samples from
  arXiv  Detail & Related papers  (2024-05-31T16:18:46Z)
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
  We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID) We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-25T16:59:08Z)
• CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction [71.31516599226606]
  We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajectories. Our approach, CovariaceNet, is based on a Conditional Generative Model with Gaussian latent variables.
  arXiv  Detail & Related papers  (2021-09-07T09:38:24Z)
• Discrete Denoising Flows [87.44537620217673]
  We introduce a new discrete flow-based model for categorical random variables: Discrete Denoising Flows (DDFs) In contrast with other discrete flow-based models, our model can be locally trained without introducing gradient bias. We show that DDFs outperform Discrete Flows on modeling a toy example, binary MNIST and Cityscapes segmentation maps, measured in log-likelihood.
  arXiv  Detail & Related papers  (2021-07-24T14:47:22Z)
• Diverse Sampling for Normalizing Flow Based Trajectory Forecasting [34.01303881881315]
  We propose Diversity Sampling for Flow (DSF) to improve the quality and diversity of trajectory samples from a pre-trained flow model. DSF is easy to implement, and we show that it offers a simple plug-in improvement for several existing flow-based forecasting models.
  arXiv  Detail & Related papers  (2020-11-30T18:23:29Z)
• Haar Wavelet based Block Autoregressive Flows for Trajectories [129.37479472754083]
  Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. We introduce a novel Haar wavelet based block autoregressive model leveraging split couplings. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets.
  arXiv  Detail & Related papers  (2020-09-21T13:57:10Z)
• Flows for simultaneous manifold learning and density estimation [12.451050883955071]
  manifold-learning flows (M-flows) represent datasets with a manifold structure more faithfully. M-flows learn the data manifold and allow for better inference than standard flows in the ambient data space.
  arXiv  Detail & Related papers  (2020-03-31T02:07:48Z)
• Gaussianization Flows [113.79542218282282]
  We propose a new type of normalizing flow model that enables both efficient iteration of likelihoods and efficient inversion for sample generation. Because of this guaranteed expressivity, they can capture multimodal target distributions without compromising the efficiency of sample generation.
  arXiv  Detail & Related papers  (2020-03-04T08:15:06Z)
• Semi-Supervised Learning with Normalizing Flows [54.376602201489995]
  FlowGMM is an end-to-end approach to generative semi supervised learning with normalizing flows. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data.
  arXiv  Detail & Related papers  (2019-12-30T17:36:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.