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 Single Video Segmentation with Robust Graph Representation
Authors: Handong Zhao, Yun Fu
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Two open public datasets MOVi CS and Ob Mi C are used for evaluation under both intersection-over-union and F-measure metrics. The superior results compared with the state-of-the-arts demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Handong Zhao1 and Yun Fu1,2 1 Department of Electrical and Computer Engineering, Northeastern University, Boston, USA, 02115 2 College of Computer and Information Science, Northeastern University, Boston, USA, 02115 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: "For all methods, we run the publicly available code and report the best performance.", referring to baselines, but does not state that the code for their own method is open-source or provide a link. |
| Open Datasets | Yes | In this work, we select two open public datasets, MOVi CS [Chiu and Fritz, 2013] and Ob Mi C [Fu et al., 2014] |
| Dataset Splits | No | The paper mentions datasets used for evaluation but does not specify training, validation, or test splits with percentages, counts, or predefined split citations for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "publicly available code" for baselines but does not list specific software dependencies with version numbers for its own method or the experimental setup. |
| Experiment Setup | No | The paper discusses the methodology but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, epochs), optimizer settings, or other training configurations. |