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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
From ViT Features to Training-free Video Object Segmentation via Streaming-data Mixture Models
Authors: Roy Uziel, Or Dinari, Oren Freifeld
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,280 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,463 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 | We demonstrate the efficacy of the method on key benchmarks: the DAVIS-2017 and You Tube-VOS 2018 validation datasets. We report the results on two widely-used SVOS benchmarks: You Tube-VOS [46] and DAVIS 2017 [34]. We performed an ablation study (Fig. 6) to analyze the influence of different parts of the method on the performance. |
| Researcher Affiliation | Academia | Roy Uziel Ben-Gurion University of the Negev, Israel EMAIL Or Dinari Ben-Gurion University of the Negev, Israel EMAIL Oren Freifeld Ben-Gurion University of the Negev, Israel EMAIL |
| Pseudocode | No | The paper describes algorithmic steps but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/BGUCS-VIL/Training-Free-VOS. |
| Open Datasets | Yes | We report the results on two widely-used SVOS benchmarks: You Tube-VOS [46] and DAVIS 2017 [34]. |
| Dataset Splits | Yes | We demonstrate the efficacy of the method on key benchmarks: the DAVIS-2017 and You Tube-VOS 2018 validation datasets. On the DAVIS-2017 validation set (Table 1)... We also evaluated our method on the DAVIS-2017 training and test-dev sets, collectively constituting 90 additional sequences. |
| Hardware Specification | Yes | Table 4: FPS across resolutions. Comparison on Tesla V100-32GB, excluding feature extraction. |
| Software Dependencies | No | Appendix B states 'We implemented our solution in PyTorch.' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | Our baseline configuration (the centered one) is: S = 10, λ = 0.33, wρ = 15. |