Summary of Towards Scene Graph Anticipation, by Rohith Peddi et al.
Towards Scene Graph Anticipation
by Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 approach to anticipating fine-grained temporal relationships between objects in videos is introduced, leveraging object-centric representations and concepts from NeuralODE and NeuralSDE. The Scene Graph Anticipation (SGA) task is defined, and baselines are adapted from state-of-the-art scene graph generation methods. A novel method, SceneSayer, models the evolution of object interactions using continuous time perspectives and solves Ordinary Differential Equations and Stochastic Differential Equations to infer future relationships. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine watching a video and trying to predict what will happen next. This paper is about developing better ways to do just that! They’re introducing new methods for understanding how objects interact with each other over time, which can be really useful for things like predicting what might happen in a video or recognizing actions in a scene. |
