Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exploiting Competition Relationship for Robust Visual Recognition
Authors: Liang Du, Haibin Ling
AAAI 2014 | Venue PDF | 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 EMAIL |
| 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}}. |