Summary of Singulartrajectory: Universal Trajectory Predictor Using Diffusion Model, by Inhwan Bae and Young-jae Park and Hae-gon Jeon
SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model
by Inhwan Bae, Young-Jae Park, Hae-Gon Jeon
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework, called SingularTrajectory, is proposed to unify trajectory prediction across five types of tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot. These tasks differ in factors such as input path length, data split, and pre-processing methods. Despite similarities in input and output formats, specialized architectures are still required for each task due to generality issues. The framework consists of a Singular space that projects motion patterns from each task into a unified embedding space, an adaptive anchor that corrects incorrect anchors based on a traversability map, and a diffusion-based predictor that enhances prototype paths through a cascaded denoising process. Experimental results on five public benchmarks demonstrate the effectiveness of SingularTrajectory in estimating general dynamics of human movements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict where people will move is developed. This method, called SingularTrajectory, can be used for different types of predictions. It works by putting all kinds of motion patterns into a special space and then using that information to make more accurate predictions. The method is tested on five real-life scenarios and shows better results than other methods. |
Keywords
* Artificial intelligence * Diffusion * Domain adaptation * Embedding space * Few shot
