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..
Weak-shot Semantic Segmentation via Dual Similarity Transfer
Authors: Junjie Chen, Li Niu, Siyuan Zhou, Jianlou Si, Chen Qian, Liqing Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on the challenging COCO-Stuff-10K and ADE20K datasets demonstrate the effectiveness of our method. |
| Researcher Affiliation | Collaboration | Junjie Chen1, Li Niu1 , Siyuan Zhou1, Jianlou Si2, Chen Qian2, Liqing Zhang1 1The Mo E Key Lab of AI, CSE department, Shanghai Jiao Tong University 2Sense Time Research, Sense Time |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/bcmi/Sim Former-Weak-Shot-Semantic-Segmentation. |
| Open Datasets | Yes | Comprehensive experiments on the challenging COCO-Stuff-10K and ADE20K datasets demonstrate the effectiveness of our method. [...] COCO-Stuff10K [3] contains 9k training images and 1k test images, covering 171 semantic classes. ADE20K [50] has 20k training images and 2k validating images, covering 150 semantic classes. |
| Dataset Splits | Yes | COCO-Stuff10K [3] contains 9k training images and 1k test images, covering 171 semantic classes. ADE20K [50] has 20k training images and 2k validating images, covering 150 semantic classes. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We use α (0.1 by default) for balancing the distillation loss and β (0.2 by default) for balancing the complementary loss. |