Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation
Authors: Beomyoung Kim, Sangeun Han, Junmo Kim1754-1761
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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) {qjadud1994, bichoomi, junmo.kim}@kaist.ac.kr |
| 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. |