One-Shot Texture Retrieval with Global Context Metric
Authors: Kai Zhu, Wei Zhai, Zheng-Jun Zha, Yang Cao
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark texture datasets and real scenarios demonstrate the abovepar segmentation performance and robust generalization across domains of our proposed method. |
| Researcher Affiliation | Academia | University of Science and Technology of China {zkzy, wzhai056}@mail.ustc.edu.cn, {zhazj, forrest}@ustc.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | We also introduce expanded experimental contents in supplementary materials1. 1https://github.com/zhukaii/OS-TR |
| Open Datasets | Yes | To validate the superiority of our model in one-shot texture segmentation task, we designed a series of experiments based on Describable Textures Dataset (DTD) [Cimpoi et al., 2014] dataset. |
| Dataset Splits | No | The paper describes training and test set splits, but does not explicitly mention a dedicated validation set split or its size. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'pytorch' for reproduction, but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Our model uses the SGD optimizer during the training process. The initial learning rate is set to 0.001 and the attenuation rate is set to 0.0005. The model stops training after 1000 epochs, where each epoch synthesizes 240 query images. All images are resized to 256 256 size and the batch size is set to 16. |