Predicting dominance in multi-person videos
Authors: Chongyang Bai, Maksim Bolonkin, Srijan Kumar, Jure Leskovec, Judee Burgoon, Norah Dunbar, V. S. Subrahmanian
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our models against four competing algorithms in the literature on two datasets and show that our results improve past performance. We show 2.4% to 16.7% improvement in AUC compared to baselines on one dataset, and a gain of 0.6% to 8.8% in accuracy on the other. Ablation testing shows that Dominance Rank features play a key role. |
| Researcher Affiliation | Academia | 1Dartmouth College 2Stanford University 3University of Arizona 4University of California Santa Barbara |
| Pseudocode | Yes | GDP s pseudo-code is shown as Algorithm 1 and also on Figure 2. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | In addition to the Resistance dataset, we used the ELEA dataset developed by [Sanchez-Cortes et al., 2012]. |
| Dataset Splits | Yes | We split the Resistance dataset into 10 folds by games. As each player appears in only one game, we always make predictions about the dominance of players in games that we have not seen before. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper mentions tools like 'Amazon s Rekognition' and 'Open Face' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Our classifier suite for binary prediction tasks consists of the 5 classifiers: k-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Linear SVM, and Random Forest. We used two classifiers: Multilayer Perceptron (MLP) with two layers, and Random Forest (RF) with 50 estimators. |