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..
Semantic Graph Construction for Weakly-Supervised Image Parsing
Authors: Wenxuan Xie, Yuxin Peng, Jianguo Xiao
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed semantic graphs not only capture more semantic relevance, but also perform significantly better than conventional graphs in image parsing. |
| Researcher Affiliation | Academia | Wenxuan Xie and Yuxin Peng and Jianguo Xiao Institute of Computer Science and Technology, Peking University, Beijing 100871, China EMAIL |
| Pseudocode | No | The paper describes methods through mathematical formulations and text, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on two standard datasets: PASCAL VOC 07 (PASCAL for short) (Everingham et al. 2010) and MSRC-21 (Shotton et al. 2009). |
| Dataset Splits | No | The paper states "In the weakly-supervised image parsing task, we assume all the image-level labels are known for both training and test set", but does not specify any explicit training/validation/test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'SLIC algorithm' and 'SIFT' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | In the experiments, we discover that the parameter k in all k-NN-based graphs are relatively insensitive to the performance, and we set k = 20 empirically. |