Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
Authors: Yongqing Liang, Xin Li, Navid Jafari, Jim Chen
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, Table 1: The quantitative evaluation on the validation set of the DAVIS17 benchmark [28] in percentages. |
| Researcher Affiliation | Academia | Yongqing Liang, Xin Li , Navid Jafari Louisiana State University {ylian16, xinli, njafari}@lsu.edu Qin Chen Northeastern University q.chen@northeastern.edu |
| Pseudocode | No | The paper describes algorithmic steps in prose but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/xmlyqing00/AFB-URR. |
| Open Datasets | Yes | We evaluated our model (AFB-URR) on DAVIS17 [28] and You Tube-VOS18 [35], two large-scale VOS benchmarks with multiple objects. Pretraining on image datasets [5, 29, 16, 19, 6] (136, 032 images in total). |
| Dataset Splits | Yes | DAVIS17 contains 60 training videos and 30 validation videos. You Tube-VOS18 (YV) contains 3, 471 training videos and 474 videos for validation. |
| Hardware Specification | Yes | We implemented our framework in Py Torch [26] and conducted experiments on a single NVIDIA 1080Ti GPU. STM [25] evaluated their work on an NVIDIA V100 GPU with 16GB memory, while we evaluated ours on a weaker machine (one NVIDIA 1080Ti GPU with 11GB memory). |
| Software Dependencies | No | The paper mentions 'Py Torch [26]' and 'Adam W [21] optimizer' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | The input frames are randomly resized and cropped into 400 400px for all training. For each training sample, we randomly select at most 3 objects for training. We minimize our loss using Adam W [21] optimizer (β = (0.9, 0.999), eps = 10 8, and the weight decay is 0.01). The initial learning rate is 10 5 for pretraining and 4 10 6 for main training. |