Boundary Knowledge Translation based Reference Semantic Segmentation
Authors: Lechao Cheng, Zunlei Feng, Xinchao Wang, Ya Jie Liu, Jie Lei, Mingli Song
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Exhaustive experiments demonstrate that, with tens of finely-grained annotated samples as guidance, Ref-Net achieves results on par with fully supervised methods on six datasets. |
| Researcher Affiliation | Academia | 1Zhejiang Lab 2Zhejiang University 3National University of Singapore 4Zhejiang University Of Technology |
| Pseudocode | No | The paper describes its methods using text and equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The target datasets we adopted contain Cityscapes, SBD, THUR, Bird, Flower, Human. What s more, the open-source datasets (MSRA10K, MSRA-B, CSSD, ECSSD, DUT-OMRON, PASCAL-Context, HKU-IS, SOD, SIP1K) are merged into Mix All, which contains multiple categories. |
| Dataset Splits | No | The paper mentions using 'ten labeled samples' for certain settings and datasets like Cityscapes, SBD, THUR, Bird, Flower, Human, but it does not specify explicit training, validation, and test splits (e.g., percentages, counts, or references to standard splits). |
| Hardware Specification | No | The paper states the model used is 'Deeplab V3+ (backbone: resnet50)' but does not provide any specific hardware details such as GPU or CPU models, or other computing resources used for experiments. |
| Software Dependencies | No | The paper mentions 'Deeplab V3+' and 'resnet50' as the network architecture and 'Adam' as the optimizer, but it does not provide specific software dependencies with version numbers (e.g., deep learning frameworks, libraries, or operating systems). |
| Experiment Setup | Yes | The parameters are set as follows: τ = 1, λ = 10, ξ = 1, ζ = 1, η = 1. In the generation of pseudo samples, the disk strel of radius r for the dilation and erosion operation is randomly sampled integer between 11 and 55. The interval iteration number between the segmentation network and discriminators is 5, the batch size is 64, Adam hyperparameters for two discriminators α = 0.0001, β1 = 0, β2 = 0.9. The learning rate for the segmentation network and two discriminators are set as 1e 4. |