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 [1].
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
Authors: Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on challenging benchmarks of DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is beneficial to segmentation quality. |
| Researcher Affiliation | Collaboration | Yuxi Li Shanghai Jiao Tong University Shanghai, China EMAIL Ning Xu Adobe Research San Jose, CA EMAIL Jinlong Peng Tencent Youtu Lab Shanghai, China EMAIL John See Multimedia University Selangor, Malaysia EMAIL Weiyao Lin Shanghai Jiao Tong University Shanghai, China EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for their methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | Datasets. We train and evaluate our method on two widely used benchmarks for semi-supervised video object segmentation, DAVIS17 [10] and Youtube-VOS [11]. |
| Dataset Splits | Yes | DAVIS17 contains 120 video sequences in total with at most 10 objects in a video. The dataset is split into 60 sequences for training, 30 for validation and the other 30 for test. The Youtube-VOS is larger in scale and contains more object categories. There are a total of 3,471 video sequences for training and 474 videos for validation in this dataset with at most 12 objects in a video. |
| Hardware Specification | Yes | The training and inference procedures are deployed on an NVIDIA TITAN Xp GPU. |
| Software Dependencies | No | The paper mentions software components like Resnet50, Image Net, and Adam optimizer, but does not provide specific version numbers for these or other libraries/frameworks. |
| Experiment Setup | Yes | We set the hyperparameters as γ = 1.0, N = 10, K = 5, and M = 50. The network is trained with a batch size of 4 for 240 epochs in total and is optimized by the Adam optimizer [22] of learning rate 10 5 and β1 = 0.9, β2 = 0.999. In both training and inference stages, the input frames are resized to the resolution of 240 427. |