Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
Authors: Yongqing Liang, Xin Li, Navid Jafari, Jim Chen
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,352 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,831 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Qin Chen Northeastern University EMAIL |
| 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. |