Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
InstanceFormer: An Online Video Instance Segmentation Framework
Authors: Rajat Koner, Tanveer Hannan, Suprosanna Shit, Sahand Sharifzadeh, Matthias Schubert, Thomas Seidl, Volker Tresp
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 15,954 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,620 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 evaluate Instance Former on three challenging datasets OVIS (Qi et al. 2021), YTVIS19 (Yang et al. 2019), and YTVIS-21 (Xu et al. 2021). ... Here, we analyze the quantitative and qualitative findings of our experiments across multiple dataset. |
| Researcher Affiliation | Academia | 1 Ludwig Maximilian University of Munich 2 Technical University of Munich 3 MCML |
| Pseudocode | No | The paper describes its methodology in narrative text and with equations, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | Code is available at https: //github.com/rajatkoner08/Instance Former. |
| Open Datasets | Yes | We evaluate Instance Former on three challenging datasets OVIS (Qi et al. 2021), YTVIS19 (Yang et al. 2019), and YTVIS-21 (Xu et al. 2021). ... We first pre-train our network on COCO (Lin et al. 2014) for 12 epochs. |
| Dataset Splits | Yes | We evaluate our model on the official validation split. |
| Hardware Specification | Yes | We train our network using the Adam W optimizer on 4 NVIDIA RTX A6000 GPUs with a batch size of 4 and learning rate of 1e 4 for 16 epochs. |
| Software Dependencies | No | The paper mentions using specific models and optimizers (e.g., 'Deformable-DETR', 'ResNet-50', 'Adam W optimizer') but does not specify software library version numbers (e.g., 'PyTorch 1.9') which would be needed for full reproducibility. |
| Experiment Setup | Yes | We train our network using the Adam W optimizer on 4 NVIDIA RTX A6000 GPUs with a batch size of 4 and learning rate of 1e 4 for 16 epochs. ... In our experiments, we have set the size of memory queue d = 4 and number of memory token per frame to 10. |