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..
SOLQ: Segmenting Objects by Learning Queries
Authors: Bin Dong, Fangao Zeng, Tiancai Wang, Xiangyu Zhang, Yichen Wei
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Industry | Bin Dong Fangao Zeng Tiancai Wang Xiangyu Zhang Yichen Wei MEGVII Technology EMAIL |
| Pseudocode | No | The paper describes methods and processes but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github. com/megvii-research/SOLQ. |
| Open Datasets | Yes | We validate our method on COCO benchmark [19]. COCO contains 115k images for training, 5k for validation and 20k for testing, involving 80 object categories with instance-level segmentation annotations. |
| Dataset Splits | Yes | COCO contains 115k images for training, 5k for validation and 20k for testing, involving 80 object categories with instance-level segmentation annotations. |
| Hardware Specification | Yes | All experiments are conducted over 8 Tesla V100 GPUs with batch size 32 except the comparison in Sec. 4.4. |
| Software Dependencies | No | The paper mentions software components like ResNet, Deformable DETR, and Adam optimizer but does not provide specific version numbers for these or other key software dependencies. |
| Experiment Setup | Yes | For the mask branch, nk is set to 256, hidden dim of MLP is 1024 and λvec is 3.0. Following DETR, λcls = 2, λL1 = 5, λgiou = 2. We train our model with Adam optimizer with weight decay of 1.0 10 4. Models are trained for 50 epochs with the initial learning rate 2.0 10 4 and decayed at 40th epoch by a factor 0.1. Multi-scale training is adopted, where the shorter side is randomly chosen within [408, 800] and the longer side is less or equal to 1333. All experiments are conducted over 8 Tesla V100 GPUs with batch size 32 except the comparison in Sec. 4.4. |