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..
Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection
Authors: Chao Zhu, Yuxin Peng
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach is evaluated on the challenging Caltech pedestrian benchmark, and outperforms other state-of-the-art on different resolution-specific test sets. |
| Researcher Affiliation | Academia | Chao Zhu and Yuxin Peng Institute of Computer Science and Technology, Peking University Beijing 100871, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Group Cost-Sensitive Ada Boost |
| Open Source Code | No | The paper mentions evaluation software but does not provide access to the source code for the methodology described in this paper. |
| Open Datasets | Yes | The experiments are conducted on the Caltech pedestrian detection benchmark (Dollár et al. 2012) |
| Dataset Splits | Yes | The pedestrian detector is trained on the training set (set00-set05), and the detection results are reported on the test set (set06-set10). ... The optimal value of the costs for different groups are selected from Cfp = 1, Cfnh [1 : 0.1 : 10] and Cfnl [Cfnh : 0.1 : Cfnh + 10] by cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (like CPU/GPU models or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using LDCF detector and Ada Boost but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The optimal value of the costs for different groups are selected from Cfp = 1, Cfnh [1 : 0.1 : 10] and Cfnl [Cfnh : 0.1 : Cfnh + 10] by cross-validation. 4096 weak classifiers are trained and combined to a strong classifier... Three bootstrapping stages are applied with 25,000 additional hard negatives each time. |