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..
SOHES: Self-supervised Open-world Hierarchical Entity Segmentation
Authors: Shengcao Cao, Jiuxiang Gu, Jason Kuen, Hao Tan, Ruiyi Zhang, Handong Zhao, Ani Nenkova, Liangyan Gui, Tong Sun, Yu-Xiong Wang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we thoroughly evaluate SOHES on various datasets and examine the Vi T-based backbone improvement for downstream tasks. We perform a series of ablation study experiments to demonstrate the efficacy of modules and steps in SOHES. |
| Researcher Affiliation | Collaboration | Shengcao Cao1 Jiuxiang Gu2 Jason Kuen2 Hao Tan2 Ruiyi Zhang2 Handong Zhao2 Ani Nenkova2 Liang-Yan Gui1 Tong Sun2 Yu-Xiong Wang1 1University of Illinois Urbana-Champaign 2Adobe Research |
| Pseudocode | No | The paper describes its method in a step-by-step manner (e.g., Step 1, Step 2, Step 3, Step 4) and includes figures illustrating the process, but it does not present formal pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper provides a "Project page: https://SOHES.github.io." However, this is a project page URL, not a direct link to a source-code repository (e.g., github.com/user/repo) nor an explicit statement confirming code release in supplementary materials or similar. |
| Open Datasets | Yes | We train our SOHES model on the SA-1B (Kirillov et al., 2023) dataset. [...] For evaluation purposes, we test SOHES on various image datasets with segmentation mask annotations in a zero-shot manner (...) MS-COCO (Lin et al., 2014), LVIS (Gupta et al., 2019), ADE20K (Zhou et al., 2017), Entity Seg (Qi et al., 2023), and SA-1B (Kirillov et al., 2023). |
| Dataset Splits | No | The paper specifies training and evaluation splits (2% for training, 0.1% for evaluation on SA-1B) but does not explicitly mention a distinct 'validation' dataset split for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | The model is trained on 8 compute nodes, each equipped with 8 NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions specific models (e.g., DINO, Vi T-Adapter, Mask2Former, Cascade PSP) and an optimizer (Adan), but it does not specify version numbers for general software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | The total batch size is 128, and the number of training steps is 40,000. We optimize the model with the Adan optimizer (Xie et al., 2022) and a base learning rate of 0.0008. [...] The teacher is updated as the exponential moving average of the student, with momentum m = 0.9995. [...] In the dynamic threshold, we set θscore, large = 0.7, θscore, small = 0.3, γ = 200. |