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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Siamese Network with Interactive Transformer for Video Object Segmentation
Authors: Meng Lan, Jing Zhang, Fengxiang He, Lefei Zhang1228-1236
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three challenging benchmarks validate the superiority of SITVOS over state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Meng Lan1, Jing Zhang2, Fengxiang He3, Lefei Zhang1,4* 1 Wuhan University 2 The University of Sydney 3 JD Explore Academy, China 4 Hubei Luojia Laboratory |
| Pseudocode | No | The paper describes algorithms and architectures in prose and figures, but does not include any specific pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | Code: https://github.com/LANMNG/SITVOS. |
| Open Datasets | Yes | MS-COCO dataset (Lin et al. 2014). ... DAVIS 2017 (Pont-Tuset et al. 2017) and You Tube-VOS (Xu et al. 2018). |
| Dataset Splits | Yes | DAVIS 2016-Val for single-object segmentation, DAVIS 2017-Val and You Tube-VOS validation sets for multi-object segmentation. |
| Hardware Specification | Yes | SITVOS is implemented in Pytorch and trained using RTX 2080Ti GPU. |
| Software Dependencies | No | SITVOS is implemented in Pytorch. The specific version number for Pytorch or any other software dependencies is not provided. |
| Experiment Setup | Yes | The input image size is 384 x 384 and batchsize is 4 for both training stages. We minimize the cross-entropy loss using the Adam optimizer with a learning rate starting at 1e-5. The learning rate is adjusted with polynomial scheduling using the power of 0.9. All batch normalization layers in the backbone are fixed as their Image Net pre-trained parameters during training. |