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..
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation
Authors: Jie Qin, Jie Wu, Xuefeng Xiao, Lujun Li, Xingang Wang2117-2125
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label. Experiments also reveal that our scheme is plug-and-play and can be incorporated with other approaches to boost their performance. Our code is available at: https://github.com/jieqin-ai/AMR. |
| Researcher Affiliation | Collaboration | Jie Qin1,2,3*, Jie Wu2, Xuefeng Xiao2, Lujun Li3, Xingang Wang3 1 School of Arti๏ฌcial Intelligence, University of Chinese Academy of Sciences 2 Byte Dance Inc 3 Institute of Automation, Chinese Academy of Sciences |
| Pseudocode | No | The paper does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/jieqin-ai/AMR. |
| Open Datasets | Yes | We evaluate our approach on the PASCAL VOC2012 dataset (Everingham et al. 2015). |
| Dataset Splits | Yes | Following the common methods (Wei et al. 2017; Wang et al. 2020b), we use 10,582 images for training, 1,449 images for validation, and 1,456 ones for testing. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU model, CPU type) used for running the experiments. |
| Software Dependencies | No | The paper mentions using ResNet50 and Deep Lab-v2 backbones, but does not list specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We train the network for 8 epochs with a batch size of 16. The initial learning rate is set to 0.01 with a momentum of 0.9. We leverage the stochastic gradient descent algorithm for network optimization with a 0.0001 weight decay. |