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..
A Simple Image Segmentation Framework via In-Context Examples
Authors: Yang Liu, Chenchen Jing, Hengtao Li, Muzhi Zhu, Hao Chen, Xinlong Wang, Chunhua Shen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various segmentation tasks show the effectiveness of the proposed method. Our code is released at: https://github.com/aim-uofa/SINE |
| Researcher Affiliation | Collaboration | Yang Liu1, Chenchen Jing1, Hengtao Li1, Muzhi Zhu1 Hao Chen1 , Xinlong Wang3, Chunhua Shen1,2 1Zhejiang University, China 2Ant Group 3Beijing Academy of Artificial Intelligence |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is released at: https://github.com/aim-uofa/SINE |
| Open Datasets | Yes | Training Data We train our model with a diverse set of segmentation datasets, including semantic, instance, and panoptic segmentation. Specifically, we utilize three visual perception datasets: ADE20K [65] is a popular semantic segmentation dataset... COCO [31] is a widely-used dataset... Objects365 [51] is a large-scale high-quality object detection dataset. |
| Dataset Splits | Yes | ADE20K [65] is a popular semantic segmentation dataset... It has 25K images, including 20K for training, 2K for validation, and 3K for testing. |
| Hardware Specification | Yes | Our model is trained for 5 days by using 8 NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions software like DINOv2 and Adam optimizer, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We train SINE about 50K steps with 64 batch sizes. We use Adam [36] optimizer and employ β1 = 0.9, β2 = 0.999 for optimization. We use a linear learning rate scheduler with a base learning rate of 1e 4 and a warmup of 100 steps. The weight decay is set to 0.05. For data augmentation, we use random horizontal flipping and the large-scale jittering (LSJ) [13] augmentation with a random scale sampled from range 0.1 to 2.0 followed by a fixed size crop to 896 896. |