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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Object Localization based on Structural SVM using Privileged Information
Authors: Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han
NeurIPS 2014 | Venue PDF | LLM Run Details | Input Tokens: 14,549 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,978 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the proposed algorithm to the Caltech-UCSD Birds 200-2011 dataset, and obtain encouraging results suggesting further investigation into the benefit of privileged information in structured prediction. We evaluate our method by learning to localize birds in the Caltech-UCSD Birds 200-2011 (CUB-2011) dataset and exploiting attributes and segmentation masks as privileged information in addition to standard visual features. |
| Researcher Affiliation | Academia | Jan Feyereisl, Suha Kwak , Jeany Son, Bohyung Han Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea EMAIL, EMAIL Current affiliation: INRIA WILLOW Project, Paris, France; e-mail: EMAIL |
| Pseudocode | Yes | Algorithm 1 Cutting plane method for solving Eq. (6) |
| Open Source Code | No | The paper does not explicitly provide a link to its source code or state that it will be made publicly available. |
| Open Datasets | Yes | Empirical evaluation of our method is performed on the Caltech-UCSD Birds 2011 (CUB-2011) [5] fine-grained categorization dataset. |
| Dataset Splits | Yes | As a validation set, 500 training images chosen at random from categories other than the ones used for training are used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'bag-of-visual-words model based on Speeded Up Robust Features (SURF) [26]' but does not provide specific version numbers for any software libraries, frameworks, or programming languages used for implementation. |
| Experiment Setup | Yes | In all experiments we tune the hyperparameters C, λ and ρ on a 4 4 4 space spanning values [2 8, ..., 25]. |