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 [1].
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. |