Inferring action structure and causal relationships in continuous sequences of human action.

By Daphna Buchsbaum, T. Griffiths, Dillon Plunkett, Alison Gopnik, Dare A. Baldwin. 2015

49 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Cultural Studies, Developmental and Educational Psychology, Causal Inference & Discovery

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. Tat-seng Chua
2. Angela Yao
3. Josh Tenenbaum
4. Chris L. Baker
5. Xindi Shang
6. Junbin Xiao
7. Xiangnan He
8. Tomer D. Ullman

In Computer Vision and Pattern Recognition

1. Tat-seng Chua
2. Angela Yao
3. Luc Van Gool
4. Junbin Xiao
5. Jitendra Malik
6. Dacheng Tao
7. Bernt Schiele
8. Josh Tenenbaum

In Cultural Studies

1. Josh Tenenbaum
2. Tomer D. Ullman
3. Brenden M. Lake
4. Laura Schulz
5. Yangqing Jia
6. Michael D. Lee

In Developmental and Educational Psychology

1. Josh Tenenbaum
2. Chris L. Baker
3. Tomer D. Ullman
4. Noah D. Goodman
5. Julian Jara-Ettinger
6. T. Griffiths
7. Rebecca Saxe
8. Fiery Cushman

In Causal Inference & Discovery

1. Tat-seng Chua
2. Josh Tenenbaum

Citations in the corpus

• NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (2021, background): “Actions in videos are often not independent but rather related with causal and temporal relationships [3].”
• Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model (2015, methodology): “We use the Dirichlet process (DP) to express the distribution over subgoal sequences, following (Buchsbaum et al. 2015).”