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..
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation
Authors: Beomyoung Kim, Sangeun Han, Junmo Kim1754-1761
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of our approach. achieves m Io U 71.4% on the PASCAL VOC 2012 segmentation benchmark using only image-level labels. |
| Researcher Affiliation | Academia | Beomyoung Kim, Sangeun Han, Junmo Kim Korea Advanced Institute of Science and Technology (KAIST) EMAIL |
| Pseudocode | Yes | Algorithm 1: Discriminative Region Suppression |
| Open Source Code | No | The paper mentions using 'Deep Lab-Large-FOV code1 and Deep Lab-ASPP code2 implemented based on the Pytorch framework' with GitHub links in footnotes (1https://github.com/wangleihitcs/Deep Lab-V1-Py Torch, 2https://github.com/kazuto1011/deeplab-pytorch). However, these are external codebases used by the authors, not their own source code for the proposed DRS method. |
| Open Datasets | Yes | We demonstrate the effectiveness of the proposed approach on the PASCAL VOC 2012 segmentation benchmark dataset (Everingham et al. 2014) |
| Dataset Splits | Yes | Following the common practice in previous works, the training set is augmented to 10,582 images. We evaluate the performance of our model using the mean intersection-over-union (m Io U) metric and compare it with other state-of-the-art methods on the validation (1,449 images) and test set (1,456 images). |
| Hardware Specification | Yes | All experiments are performed on NVIDIA TITAN XP. |
| Software Dependencies | No | Our method is implemented on Pytorch (Paszke et al. 2017). The paper mentions PyTorch but does not specify a version number for it or other software dependencies. |
| Experiment Setup | Yes | The initial learning rate is set to 1e-3 and is decreased by a factor of 10 at epoch 5 and 10. For data augmentation, we apply a random crop with 321 321 size, random horizontal flipping, and random color jittering. We use a batch size of 5 and train the classification network for 15 epochs. We optimize the refinement network for the refinement learning with MSE loss using Adam (Kingma and Ba 2014) optimizer with a learning rate of 1e-4. The batch size is 5, the total training epoch is 15, and the learning rate is dropped by a factor of 10 at epoch 5 and 10. When generating pseudo segmentation labels, we empirically choose α = 0.2 for object cues and β = 0.06 for background cues. |