Semantic Single Video Segmentation with Robust Graph Representation
Authors: Handong Zhao, Yun Fu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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. |