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..
Cross-Granularity Graph Inference for Semantic Video Object Segmentation
Authors: Huiling Wang, Tinghuai Wang, Ke Chen, Joni-Kristian Kämäräinen
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on two popular semantic video object segmentation benchmarks and demonstrate that it advances the state-of-the-art by achieving superior accuracy performance than other leading methods. |
| Researcher Affiliation | Collaboration | Huiling Wang1, Tinghuai Wang2, Ke Chen1, Joni-Kristian K am ar ainen1 1Department of Signal Processing, Tampere University of Technology, Finland 2Nokia Technologies, Finland {huiling.wang, ke.chen, joni.kamarainen}@tut.fi, EMAIL |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. While it describes steps of the proposed methods and provides equations, it lacks the structured formatting of pseudocode. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate on two large-scale video object segmentation datasets, You Tube-Objects [Prest et al., 2012], ego Motion [Shankar Nagaraja et al., 2015], which are totally over 30,000 frames. The categories of these two datasets are subsets of the pretrained 20 classes of PASCAL VOC 2012 in R-CNN. |
| Dataset Splits | No | The paper uses standard datasets but does not explicitly provide specific training/test/validation dataset splits (e.g., percentages, sample counts, or explicit standard split names for their own setup). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various models and algorithms used (e.g., Fast R-CNN, SR-DCF tracker, VGG-16 Net, alpha expansion) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper does not provide specific details on the experimental setup, such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific training configurations. |