Summary of 4d Panoptic Scene Graph Generation, by Jingkang Yang et al.
4D Panoptic Scene Graph Generation
by Jingkang Yang, Jun Cen, Wenxuan Peng, Shuai Liu, Fangzhou Hong, Xiangtai Li, Kaiyang Zhou, Qifeng Chen, Ziwei Liu
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper introduces 4D Panoptic Scene Graph (PSG-4D), a novel representation that bridges the gap between raw visual data and high-level visual understanding in dynamic 4D environments. The authors propose PSG-4DFormer, a Transformer-based model that predicts panoptic segmentation masks, tracks masks along the time axis, and generates scene graphs via a relation component. To facilitate research, they build a richly annotated PSG-4D dataset with over 1M frames, each labeled with 4D panoptic segmentation masks and fine-grained scene graphs. The authors demonstrate their method’s effectiveness on this new dataset and provide a real-world application example that integrates a large language model into the PSG-4D system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine living in a world where you can see and understand everything around you, including objects moving through time. This paper helps computers do just that by creating a special way to understand 4D environments, which combine visual data with information about what’s happening over time. The authors create a new dataset with lots of labeled examples and develop a model that can predict what’s happening in these environments. They show that their method works well on this dataset and demonstrate how it could be used in real-life applications. |
Keywords
» Artificial intelligence » Large language model » Transformer
