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 | Archive PDF | Plain Text | LLM Run Details
| 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. |