Exploiting Competition Relationship for Robust Visual Recognition
Authors: Liang Du, Haibin Ling
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In both experiments our approach demonstrates promising performance gains by exploiting the between-task competition. |
| Researcher Affiliation | Academia | Center for Data Analytics and Biomedical Informatics Department of Computer and Information Science Temple University Philadelphia, PA, 19122, USA {liang.du, hbling}@temple.edu |
| Pseudocode | Yes | Algorithm 1 Comp Boost |
| Open Source Code | Yes | We will release the source code for the Comp Boost algorithm at http://www.dabi.temple.edu/ hbling/code/competingtask.htm. |
| Open Datasets | Yes | For actor independent expression recognition, we use the Japanese Female Facial Expression (JAFFE) database (Lyons et al. 1998) as our testbed. |
| Dataset Splits | Yes | Five-fold cross validation strategy is applied in the training data to automatically determining the parameter λ in (4) in the candidate set of {λ = m 10k : k { 2, 1, 0}, m {1, 2, 5, 8}}. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Open CV implementation of the Viola-Jones face detector' but does not specify any version numbers for software dependencies. |
| Experiment Setup | Yes | In all experiments, we use 100 weak learners in our algorithm, i.e., M = 100. Five-fold cross validation strategy is applied in the training data to automatically determining the parameter λ in (4) in the candidate set of {λ = m 10k : k { 2, 1, 0}, m {1, 2, 5, 8}}. |