MaskRNN: Instance Level Video Object Segmentation
Authors: Yuan-Ting Hu, Jia-Bin Huang, Alexander Schwing
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them. |
| Researcher Affiliation | Academia | Yuan-Ting Hu UIUC ythu2@illinois.edu Jia-Bin Huang Virginia Tech jbhuang@vt.edu Alexander G. Schwing UIUC aschwing@illinois.edu |
| Pseudocode | No | The paper describes the architecture and process in text and diagrams (Figure 1 and 2) but does not provide any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | We use the training set of the DAVIS dataset to pre-train the appearance network for general-purpose object segmentation. The DAVIS-2016 dataset [37] contains 30 training videos and 20 testing videos and the DAVIS-2017 dataset [39] consists of 60 training videos and 30 testing videos. |
| Dataset Splits | Yes | The quantitative evaluation on the validation set of DAVIS dataset [37]. The DAVIS-2016 dataset contains 30 training videos and 20 validation videos. The DAVIS-2017 dataset contains 60 training videos and 30 validation videos. |
| Hardware Specification | No | We thank NVIDIA for providing the GPUs used in this research. This statement does not specify the model or number of GPUs used. |
| Software Dependencies | No | Note that we use the pre-trained flow Net2.0 [19] for optical flow computation. The paper mentions FlowNet2.0 and Adam solver but does not provide version numbers for these or other software dependencies. |
| Experiment Setup | Yes | During offline training all networks are optimized for 10 epochs using the Adam solver [27] and the learning rate is gradually decayed during training, starting from 10 5. We train the network for 200 iterations, and the learning rate is gradually decayed over time. |