Modeling Occlusion by Discriminative AND-OR Structures

By Bo Li, Wenze Hu, Tianfu Wu, Song-Chun Zhu. 2013

35 citations recorded by the source corpus.

Subfields: Building and Construction, 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 Building and Construction

1. Quanshi Zhang
2. Ying Nian Wu
3. Ruiming Cao
4. Song-Chun Zhu

In Computer Vision and Pattern Recognition

1. Song-Chun Zhu
2. Quanshi Zhang
3. Ying Nian Wu
4. Luc Van Gool
5. Alan L. Yuille
6. Ruiming Cao
7. Caiming Xiong
8. Trevor Darrell

Citations in the corpus

• Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning (2016, methodology): “The third baseline was proposed in (Li et al. 2013), which trained a DPM component for each object pose to localize object parts (namely, PL-DPM).”
• Interpreting CNN knowledge via an Explanatory Graph (2017, methodology): “We also compared our method with two DPM approaches, i.e. SS-DPM-Part ) [2] and PL-DPM-Part [13] and a graphical model for part localization, namely Part-Graph [4].”
• Joint action recognition and pose estimation from video (2015, methodology): “We start by building a spatial-temporal And-Or graph model[37][23][14][13] to represent actions and poses jointly.”
• Interactively Transferring CNN Patterns for Part Localization (2017, methodology): “We also compared our method with two DPM-related methods, i.e. the strongly supervised DPM ( SS-DPM-Part ) [2] and the technique in [9] ( PL-DPM-Part ), respectively.”
• Mining Object Parts from CNNs via Active Question-Answering (2017, methodology): “The fifth and sixth baselines were the strongly supervised DPM (SS-DPM-Part) [2] and the technique in [18] (PL-DPM-Part), respectively.”