Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding

• URL: http://arxiv.org/abs/2207.14539v2
• Date: Tue, 26 Dec 2023 01:14:05 GMT
• Title: Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding
• Authors: Yan Lin, Huaiyu Wan, Shengnan Guo, Jilin Hu, Christian S. Jensen, Youfang Lin
• Abstract summary: 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.
• Score: 36.18788551389281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatio-temporal trajectories provide valuable information about movement and travel behavior, enabling various downstream tasks that in turn power real-world applications. Learning trajectory embeddings can improve task performance but may incur high computational costs and face limited training data availability. Pre-training learns generic embeddings by means of specially constructed pretext tasks that enable learning from unlabeled data. Existing pre-training methods face (i) difficulties in learning general embeddings due to biases towards certain downstream tasks incurred by the pretext tasks, (ii) limitations in capturing both travel semantics and spatio-temporal correlations, and (iii) the complexity of long, irregularly sampled trajectories. To tackle these challenges, we propose Maximum Multi-view Trajectory Entropy Coding (MMTEC) for learning general and comprehensive trajectory embeddings. We introduce a pretext task that reduces biases in pre-trained trajectory embeddings, yielding embeddings that are useful for a wide variety of downstream tasks. We also propose an attention-based discrete encoder and a NeuralCDE-based continuous encoder that extract and represent travel behavior and continuous spatio-temporal correlations from trajectories in embeddings, respectively. Extensive experiments on two real-world datasets and three downstream tasks offer insight into the design properties of our proposal and indicate that it is capable of outperforming existing trajectory embedding methods.

Related papers

• PTR: A Pre-trained Language Model for Trajectory Recovery [31.08861372332931]
  We propose a framework called PTR to mitigate the issue of limited dense trajectory data. PTR incorporates an explicit trajectory prompt and is trained on datasets with multiple sampling intervals. We also present a trajectory embedder that encodes trajectory points and transforms the embeddings of both observed and missing points into a format comprehensible to PLMs.
  arXiv  Detail & Related papers  (2024-10-18T08:38:12Z)
• Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory [53.37473225728298]
  The rapid evolution of deep learning and large language models has led to an exponential growth in the demand for training data. Matching Training Trajectories (MTT) has been a prominent approach, which replicates the training trajectory of an expert network on real data with a synthetic dataset. We introduce a novel method called Matching Convexified Trajectory (MCT), which aims to provide better guidance for the student trajectory.
  arXiv  Detail & Related papers  (2024-06-28T11:06:46Z)
• Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond [58.63558696061679]
  Trajectory computing is crucial in various practical applications such as location services, urban traffic, and public safety. We present a review of development and recent advances in deep learning for trajectory computing (DL4Traj) Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold potential to augment trajectory computing.
  arXiv  Detail & Related papers  (2024-03-21T05:57:27Z)
• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations [22.23114883485924]
  We propose a novel algorithm called GENTLE for learning generalizable task representations in the face of data limitations. GENTLE employs Task Auto-Encoder(TAE), which is an encoder-decoder architecture to extract the characteristics of the tasks. To alleviate the effect of limited behavior diversity, we construct pseudo-transitions to align the data distribution used to train TAE with the data distribution encountered during testing.
  arXiv  Detail & Related papers  (2023-12-26T07:02:12Z)
• SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations [76.45009891152178]
  Pretraining-finetuning approach can alleviate the labeling burden by fine-tuning a pre-trained backbone across various downstream datasets as well as tasks. We show, for the first time, that general representations learning can be achieved through the task of occupancy prediction. Our findings will facilitate the understanding of LiDAR points and pave the way for future advancements in LiDAR pre-training.
  arXiv  Detail & Related papers  (2023-09-19T11:13:01Z)
• Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks [65.23947618404046]
  We introduce a framework that acquires goal-conditioned policies for unseen temporally extended tasks via offline reinforcement learning on broad data. When faced with a novel task goal, the framework uses an affordance model to plan a sequence of lossy representations as subgoals that decomposes the original task into easier problems. We show that our framework can be pre-trained on large-scale datasets of robot experiences from prior work and efficiently fine-tuned for novel tasks, entirely from visual inputs without any manual reward engineering.
  arXiv  Detail & Related papers  (2022-10-12T21:46:38Z)
• Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
  Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
  arXiv  Detail & Related papers  (2021-07-19T15:56:01Z)

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.