Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Authors: Zhipeng Wang, Hao Wang, Jiexi Yan, Aming Wu, Cheng Deng
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our approach notably outperforms the state-of-the-art methods in both Sketchy and TUBerlin datasets. |
| Researcher Affiliation | Academia | Xidian University {zpwang1996, haowang.xidian}@gmail.com, jxyan@stu.xidian.edu.cn, amwu@xidian.edu.cn, chdeng.xd@gmail.com |
| Pseudocode | No | The paper describes the methods using prose and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it explicitly state that the code is being released. |
| Open Datasets | Yes | To verify the effectiveness of our method, We evaluate our DSN method on two popular SBIR datasets, i.e., Sketchy [Sangkloy et al., 2016] and TU-Berlin [Eitz et al., 2012]. |
| Dataset Splits | Yes | For comparison, we follow the split of [Shen et al., 2018] to randomly choose 25 categories and the rest 100 categories for testing and training in Sketchy. For TU-Berlin, 30 categories and the rest 220 categories are chosen for testing and training. |
| Hardware Specification | Yes | We implement our method with Py Torch on two TITAN RTX GPUs. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not specify a version number for this or any other software dependency. |
| Experiment Setup | Yes | The initial learning rate is set to 0.0001 and exponentially decays to 1e 7 in training. The batch size in our original setting is 96 and the training epochs is 10. Usually, we set hyperparameters λ1 = 0.1, λ2 = 1 and λ3 = 1 unless stated. For cross-modal contrastive method, we set the temperature τ as 0.07 and the dimension of contrastive vectors is 128 in all the experiments. For category-specific memory bank, we set k = 10. |