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..
TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation
Authors: Rongkun Zheng, Lu Qi, Xi Chen, Yi Wang, Kun Wang, Yu Qiao, Hengshuang Zhao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experimental evaluations on four popular and challenging benchmarks, including You Tube-VIS 2019, You Tube VIS 2021, OVIS, and UVO. Our model shows significant improvement over the baseline solutions, and sets new state-of-the-art records on all benchmarks. |
| Researcher Affiliation | Collaboration | 1The University of Hong Kong 2University of California, Merced 3Shanghai Artificial Intelligence Laboratory 4Sense Time Research |
| Pseudocode | No | The paper describes its methodology in text and uses figures to illustrate the framework, but it does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/rkzheng99/TMT-VIS. |
| Open Datasets | Yes | We conduct extensive experimental evaluations on four popular and challenging benchmarks, including You Tube-VIS 2019 and 2021 [47], OVIS [35], and UVO [39]. |
| Dataset Splits | Yes | You Tube-VIS 2019 [47] is the first large-scale dataset for video instance segmentation, with 2.9K videos averaging 4.61s in duration and 27.4 frames in validation videos. You Tube-VIS 2021 [47] includes more challenging longer videos with more complex trajectories, resulting in an average of 39.7 frames in validation videos. Table 3: Ablation study on training with multiple VIS datasets with Mask2Former-VIS (which is abbreviated as M2F ) and TMT-VIS and their validation results on various VIS datasets. |
| Hardware Specification | No | The paper mentions model backbones (e.g., Res Net-50, Swin-L) but does not provide specific details about the hardware (GPU, CPU models, memory, etc.) used for running the experiments. |
| Software Dependencies | No | Our method is implemented on top of detectron2 [46]. |
| Experiment Setup | Yes | Our method is implemented on top of detectron2 [46]. Hyper-parameters regarding the pixel decoder and transformer decoder are consistent with the settings of Mask2Former-VIS [7]. In the Taxonomy Compilation Module, the size of the taxonomy embedding set NT is set to 10, which matches the maximum instance number per video. ... we set λcls = 2.0 and λtaxo = 0.5. ... During inference, we resize the shorter side of each frame to 360 pixels for Res Net [14] backbones and 480 pixels for Swin [29] backbones. |