Evaluating information contributions of bottom-up and top-down processes

By Xiong Yang, Tianfu Wu, Song-Chun Zhu. 2009

10 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

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

In Artificial Intelligence

1. Song-Chun Zhu
2. Quanshi Zhang
3. Wenguan Wang
4. Xiong Yang
5. Ying Nian Wu
6. Liang Lin
7. Mun Wai Lee
8. Benjamin Z. Yao

In Cognitive Neuroscience

1. Song-Chun Zhu
2. Liang Lin
3. Wenguan Wang
4. Benjamin Z. Yao

In Electrical and Electronic Engineering

1. Song-Chun Zhu
2. Liang Lin
3. Quanshi Zhang
4. Wenguan Wang

Citations in the corpus

• Visual interpretability for deep learning: a survey (2018, background): “This idea is related to the disentanglement of the local bottom-up, and top-down information components for prediction [Wu et al. , 2007; Yang et al. , 2009; Wu and Zhu, 2011] During the detection process, a bounding box is interpreted as the best parse tree derived from the AOG on-the-fly.”
• Examining CNN representations with respect to Dataset Bias (2017, background): “(Wu, Xia, and Zhu 2007; Yang, Wu, and Zhu 2009; Wu and Zhu 2011) tried to isolate and diagnose information from local, bottom-up, or top-down inference processes.”
• I2T: Image Parsing to Text Description (2010, methodology): “Here we extend the discussion with several improvements, namely a bottom-up/top-down inference algorithm from [65] and a cluster sampling algorithm from [51].”