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..
Mask Propagation for Efficient Video Semantic Segmentation
Authors: Yuetian Weng, Mingfei Han, Haoyu He, Mingjie Li, Lina Yao, Xiaojun Chang, Bohan Zhuang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on VSPW and Cityscapes demonstrate that our mask propagation framework achieves SOTA accuracy and efficiency trade-offs. |
| Researcher Affiliation | Collaboration | 1ZIP Lab, Monash University 2Baidu Inc. 3Re LER, AAII, UTS 4Data61, CSIRO 5Mohamed bin Zayed University of AI |
| Pseudocode | No | The paper describes the method using textual descriptions and equations, but does not provide a formal pseudocode block or algorithm. |
| Open Source Code | Yes | Code is available at https://github.com/ziplab/MPVSS. |
| Open Datasets | Yes | We evaluate our method on two benchmark datasets: VSPW [42] and Cityscapes [9]. |
| Dataset Splits | Yes | VSPW is the largest video semantic segmentation benchmark, consisting of 2,806 training clips (198,244 frames), 343 validation clips (24,502 frames), and 387 test clips (28,887 frames). |
| Hardware Specification | Yes | Frame-per-second (FPS) is measured on a single NVIDIA V100 GPU with 3 repeated runs. |
| Software Dependencies | No | The paper mentions software components like Mask2Former, Flow Net, and AdamW optimizer, but does not specify their version numbers or the versions of the programming languages/libraries used. |
| Experiment Setup | Yes | By default, all experiments are trained with a batch size of 16 on 8 NVIDIA GPUs. All the models are trained with the Adam W optimizer [41] for a maximum of 90k iterations and the polynomial learning rate decay schedule [4] with an initial learning rate of 5e-5. For our proposed models, we use 5 as the default key frame interval for comparison. |