Scene Understanding by Reasoning Stability and Safety

By Bo Zheng, Yibiao Zhao, Joey C. Yu, Katsushi Ikeuchi, Song-Chun Zhu. 2015

74 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Abhinav Kumar Gupta
2. Josh Tenenbaum
3. X. Wang
4. Li Fei-Fei
5. Christopher Bongsoo Choy
6. Song-Chun Zhu
7. Ying Nian Wu
8. Cheston Tan

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Abhinav Kumar Gupta
3. Jitendra Malik
4. Li Fei-Fei
5. Trevor Darrell
6. Dacheng Tao
7. Andrea Vedaldi
8. Xiaogang Wang

Citations in the corpus

• Learning Physical Intuition of Block Towers by Example (2016, background): “In computer vision, a number of works have used physical reasoning to aid scene understanding (Zheng et al., 2015; Koppula & Saxena, 2016).”
• Scene Graph Generation by Iterative Message Passing (2017, background): “Physical relationships, such as support and stability, have been studied in [17, 28, 42].”
• Deep Learning vs. Traditional Computer Vision (2019, methodology): “motion estimation [24], video stabilization [21], motion capture [24], video processing [21] and scene understanding [25] which cannot simply be easily implemented in a differentiable manner with deep learning but benefit from solutions using "traditional" techniques.”
• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “object localization and segmentation) must be physically plausible [96, 88, 97, 98, 36, 99]; see Figure 8.”
• Holistic++ Scene Understanding: Single-View 3D Holistic Scene Parsing and Human Pose Estimation With Human-Object Interaction and Physical Commonsense (2019, background): “By exploiting the underlying physical properties of scenes and objects, recent efforts have demonstrated the capability of estimating both current and future dynamics of static scenes [49, 28] and objects [60], understanding the support relationships and stability of objects [56], volumetric and occ”
• A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding (2016, unknown): “…in both cognitive science and computational vision (Battaglia, Hamrick, & Tenenbaum, 2013; Gupta, Efros, & Hebert, 2010; Mot-taghi, Bagherinezhad, Rastegari, & Farhadi, 2015; Fragki-adaki, Agrawal, Levine, & Malik, 2015; Zheng, Zhao, Yu, Ikeuchi, & Zhu, 2015; Li, Azimi, Leonardis, & Fritz, 2016).”
• Interpretable Intuitive Physics Model (2018, background): “Physical reasoning and learning physical commonsense has raised a lot of interest in recent years [29,17,16,28,18,1,31,5].”
• PIP: Physical Interaction Prediction via Mental Imagery with Span Selection (2021, background): “AI systems need to possess this ability before they can be safely and efficiently deployed in the physical world [3, 4, 5].”