UniTE: A Survey and Unified Pipeline for Pre-training ST Trajectory Embeddings
- URL: http://arxiv.org/abs/2407.12550v1
- Date: Wed, 17 Jul 2024 13:31:13 GMT
- Title: UniTE: A Survey and Unified Pipeline for Pre-training ST Trajectory Embeddings
- Authors: Yan Lin, Zeyu Zhou, Yicheng Liu, Haochen Lv, Haomin Wen, Tianyi Li, Yushuai Li, Christian S. Jensen, Shengnan Guo, Youfang Lin, Huaiyu Wan,
- Abstract summary: Methods for pre-training embeddings have shown promising applicability across different tasks.
We present UniTE, a survey and a unified pipeline for this domain.
We also contribute a selection of experimental results using the proposed pipeline on real-world datasets.
- Score: 32.0161907018594
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spatio-temporal (ST) trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses. Thus, the qualities of embeddings are very important. Methods for pre-training embeddings, which leverage unlabeled trajectories for training universal embeddings, have shown promising applicability across different tasks, thus attracting considerable interest. However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development new methods and the analysis of methods. To overcome these obstacles and advance the field of pre-training of trajectory embeddings, we present UniTE, a survey and a unified pipeline for this domain. In doing so, we present a comprehensive list of existing methods for pre-training trajectory embeddings, which includes methods that either explicitly or implicitly employ pre-training techniques. Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings. Additionally, we contribute a selection of experimental results using the proposed pipeline on real-world datasets.
Related papers
- Diffusion Generative Flow Samplers: Improving learning signals through
partial trajectory optimization [87.21285093582446]
Diffusion Generative Flow Samplers (DGFS) is a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments.
Our method takes inspiration from the theory developed for generative flow networks (GFlowNets)
arXiv Detail & Related papers (2023-10-04T09:39:05Z) - Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda
for Developing Practical Guidelines and Tools [18.513353100744823]
Recent work has called on the ML community to take a more holistic approach to tackle fairness issues.
We first demonstrate that without clear guidelines and toolkits, even individuals with specialized ML knowledge find it challenging to hypothesize how various design choices influence model behavior.
We then consult the fair-ML literature to understand the progress to date toward operationalizing the pipeline-aware approach.
arXiv Detail & Related papers (2023-09-29T15:48:26Z) - NormAUG: Normalization-guided Augmentation for Domain Generalization [60.159546669021346]
We propose a simple yet effective method called NormAUG (Normalization-guided Augmentation) for deep learning.
Our method introduces diverse information at the feature level and improves the generalization of the main path.
In the test stage, we leverage an ensemble strategy to combine the predictions from the auxiliary path of our model, further boosting performance.
arXiv Detail & Related papers (2023-07-25T13:35:45Z) - Towards Motion Forecasting with Real-World Perception Inputs: Are
End-to-End Approaches Competitive? [93.10694819127608]
We propose a unified evaluation pipeline for forecasting methods with real-world perception inputs.
Our in-depth study uncovers a substantial performance gap when transitioning from curated to perception-based data.
arXiv Detail & Related papers (2023-06-15T17:03:14Z) - Let Offline RL Flow: Training Conservative Agents in the Latent Space of
Normalizing Flows [58.762959061522736]
offline reinforcement learning aims to train a policy on a pre-recorded and fixed dataset without any additional environment interactions.
We build upon recent works on learning policies in latent action spaces and use a special form of Normalizing Flows for constructing a generative model.
We evaluate our method on various locomotion and navigation tasks, demonstrating that our approach outperforms recently proposed algorithms.
arXiv Detail & Related papers (2022-11-20T21:57:10Z) - Pre-training General Trajectory Embeddings with Maximum Multi-view
Entropy Coding [36.18788551389281]
Trajectory embeddings can improve task performance but may incur high computational costs and face limited training data availability.
Existing trajectory embedding methods face difficulties in learning general embeddings due to biases towards certain downstream tasks.
We propose Multi-view Trajectory Entropy Coding Coding (MMTEC) for learning general comprehensive trajectory embeddings.
arXiv Detail & Related papers (2022-07-29T08:16:20Z) - A Survey on Deep Semi-supervised Learning [51.26862262550445]
We first present a taxonomy for deep semi-supervised learning that categorizes existing methods.
We then offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences.
arXiv Detail & Related papers (2021-02-28T16:22:58Z) - Deep Shells: Unsupervised Shape Correspondence with Optimal Transport [52.646396621449]
We propose a novel unsupervised learning approach to 3D shape correspondence.
We show that the proposed method significantly improves over the state-of-the-art on multiple datasets.
arXiv Detail & Related papers (2020-10-28T22:24:07Z)
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.