Coarse-to-fine Image Co-segmentation with Intra and Inter Rank Constraints
Authors: Lianli Gao, Jingkuan Song, Dongxiang Zhang, Heng Tao Shen
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two commonly used image datasets (i Coseg and MSRC) demonstrate that CFC outperforms other state-of-the-art methods. |
| Researcher Affiliation | Academia | Center for Future Media and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China {lianli.gao, zhangdo}@uestc.edu.cn, jingkuan.song@gmail.com, shenhengtao@hotmail.com |
| Pseudocode | No | The paper describes an iterative optimization process using mathematical equations but does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any information about open-source code availability, links to repositories, or mention of code in supplementary materials. |
| Open Datasets | Yes | We evaluated the proposed algorithm on two public benchmark datasets: the i Coseg dataset [Batra et al., 2010] and the MSRC dataset [Winn et al., 2005]. |
| Dataset Splits | No | The paper mentions using subsets of datasets and evaluating metrics, but does not provide specific details on training, validation, or test splits (e.g., percentages or sample counts) needed for reproduction. It mentions "We follow [Lee et al., 2015] to use a subset of 16 object classes" and "We select the same classes as reported by [Joulin et al., 2010; Lee et al., 2015]" for dataset selection, but not split details. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Software from [van de Sande et al., 2011] is used" for feature extraction, but does not specify version numbers for any software, libraries, or programming languages used in their own implementation. |
| Experiment Setup | No | The paper mentions that "all the parameter values are empirically set as above" for refining candidate masks, and states rules for adjusting α and β, but it does not provide a comprehensive list of concrete hyperparameter values, learning rates, batch sizes, epochs, or optimizer settings typically found in an experimental setup section for reproduction. |