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..
Label-efficient Segmentation via Affinity Propagation
Authors: Wentong Li, Yuqian Yuan, Song Wang, Wenyu Liu, Dongqi Tang, Jian liu, Jianke Zhu, Lei Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three typical label-efficient segmentation tasks, i.e. box-supervised instance segmentation, point/scribble-supervised semantic segmentation and CLIP-guided semantic segmentation, demonstrate the superior performance of the proposed approach. |
| Researcher Affiliation | Collaboration | Wentong Li1 , Yuqian Yuan1 , Song Wang1, Wenyu Liu1, Dongqi Tang2, Jian Liu2, Jianke Zhu1 , Lei Zhang3 1Zhejiang University 2Ant Group 3The Hong Kong Polytechnical University |
| Pseudocode | Yes | Algorithm 1: Algorithm for GP process |
| Open Source Code | No | https://Li Wentomng.github.io/apro/ - This is a project page/personal homepage, not a direct link to a source code repository, nor is there an explicit statement about code release. |
| Open Datasets | Yes | We conduct experiments on two widely used datasets for the weakly box-supervised instance segmentation task: COCO [49]... Pascal VOC [43] augmented by SBD [50] based on the original Pascal VOC 2012 [51]... We conduct experiments on the widely-used Pascal VOC2012 dataset [51]... Pascal Context [60]... COCO-Stuff [61] |
| Dataset Splits | Yes | COCO [49], which has 80 classes with 115K train2017 images and 5K val2017 images. Pascal VOC [43] augmented by SBD [50] based on the original Pascal VOC 2012 [51], which has 20 classes with 10,582 trainaug images and 1,449 val images. |
| Hardware Specification | Yes | The experiment is conducted on a single GeForce RTX 3090 with batch size 1. |
| Software Dependencies | No | We follow the commonly used training settings on each dataset as in MMDetection [56]. The paper mentions software tools and frameworks but does not provide specific version numbers for them. |
| Experiment Setup | Yes | The initial learning rate is set to 10^-4 and the weight decay is 0.05 with 16 images per mini-batch. For Mask2Former framework [53], the large-scale jittering augmentation scheme [58] is employed with a random scale sampled within range [0.1, 2.0], followed by a fixed size crop to 1024x1024. The input size is 512x512. The SGD optimizer with momentum of 0.9 and weight decay of 10^-4 is used. The initial learning rate is 0.001, and there are 80k training iterations. |