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..
Language-driven Semantic Segmentation
Authors: Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, Rene Ranftl
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct quantitative evaluation on a variety of zeroand few-shot semantic segmentation tasks. |
| Researcher Affiliation | Collaboration | Boyi Li Cornell University, Cornell Tech Kilian Q. Weinberger Cornell University Serge Belongie University of Copenhagen Vladlen Koltun Apple René Ranftl Intel Labs |
| Pseudocode | No | The paper describes the method and architecture using natural language and diagrams, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and demo are available at https://github.com/isl-org/lang-seg. |
| Open Datasets | Yes | We evaluate on three widely-used few-shot semantic segmentation benchmarks: PASCAL-5i (Everingham et al., 2015), COCO-20i (Lin et al., 2014), and FSS-1000 (Li et al., 2020c). |
| Dataset Splits | Yes | Following the standard protocol, we split the 1000 classes into training, validation, and test classes, with 520, 240, and 240 classes, respectively. |
| Hardware Specification | Yes | We train with a batch size of 6 on six Quadro RTX 6000. |
| Software Dependencies | No | The paper mentions software like PyTorch and refers to various models and frameworks (e.g., CLIP, ViT, ResNet, DPT), but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use SGD with momentum 0.9 and a polynomial learning rate scheduler with decay rate 0.9. We train with a batch size of 6 on six Quadro RTX 6000. |