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..
Make One-Shot Video Object Segmentation Efficient Again
Authors: Tim Meinhardt, Laura Leal-Taixé
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the applicability of e-OSVOS on three semi-supervised VOS benchmarks, namely, DAVIS 2016 [27], DAVIS 2017 [28] and You Tube-VOS [41]. |
| Researcher Affiliation | Academia | Tim Meinhardt Technical University of Munich EMAIL Laura Leal-Taixé Technical University of Munich EMAIL |
| Pseudocode | Yes | In Algorithm 1 of the supplementary, we illustrate the full e-OSVOS training pipeline for a given VOS taskset Ttrain. |
| Open Source Code | Yes | Code is available at https://github.com/dvl-tum/e-osvos. |
| Open Datasets | Yes | We demonstrate the applicability of e-OSVOS on three semi-supervised VOS benchmarks, namely, DAVIS 2016 [27], DAVIS 2017 [28] and You Tube-VOS [41]. ... Mask R-CNN model with Res Net50 [12] and FPN [17] pre-trained on the COCO [18] segmentation dataset. |
| Dataset Splits | Yes | DAVIS 2016 The DAVIS 2016 [27] benchmark consists of a training and validation set with 30 and 20 single object video sequences, respectively. ... DAVIS 2017 The DAVIS 2017 [28] benchmark extends DAVIS-16 with 100 additional sequences including dedicated test-dev and test sets. The validation, test-dev and test sets each consist of 30 sequences. ... You Tube-VOS Our largest benchmark, You Tube-VOS [41], consists of 4453 video sequences including dedicated test and validation sets with 508 and 474 sequences, respectively. |
| Hardware Specification | Yes | each distributed to a Quadro RTX 6000 GPU for a total of 4 days. |
| Software Dependencies | No | The paper mentions Mask R-CNN, ResNet50, FPN, and RAdam, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | To limit the computational effort, we ignore second order derivatives and fine-tune for T = 5 BPTT iterations. ... The online adaptation (On A) is applied every IOn A = 5 steps for TOn A = 10 iterations. |