Cross-Granularity Graph Inference for Semantic Video Object Segmentation
Authors: Huiling Wang, Tinghuai Wang, Ke Chen, Joni-Kristian Kämäräinen
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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, tinghuai.wang@nokia.com |
| 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. |