Semantic Locality-Aware Deformable Network for Clothing Segmentation
Authors: Wei Ji, Xi Li, Yueting Zhuang, Omar El Farouk Bourahla, Yixin Ji, Shihao Li, Jiabao Cui
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of the proposed model against the state-of-the-art approaches. Table 1 shows the performance of our method compared with various baselines. Results show that we achieve the state-of-the-art performance in accuracy in three datasets. |
| Researcher Affiliation | Collaboration | Wei Ji1, Xi Li1,2 , Yueting Zhuang1 , Omar El Farouk Bourahla1, Yixin Ji1, Shihao Li1, Jiabao Cui1 1 Zhejiang University, Hangzhou, China 2 Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1: Clothing segmentation with semantic locality-aware deformable network |
| Open Source Code | No | The paper does not provide any statement regarding the availability of its source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | In order to evaluate the performance of the proposed approach, we conduct a set of qualitative and quantitative experiments on two benchmark datasets annotated with pixel-wise groundtruth labeling, including Fashionista [Yamaguchi et al., 2012], refined Fashionista [Tangseng et al., 2017], and CFPD [Liu et al., 2014]. |
| Dataset Splits | Yes | For the set of training and testing, we randomly divide the Fashionista dataset into train-test splits the same as [Yamaguchi et al., 2012] with 10% of training images leaving for validation, and divide CFPD dataset into 90% train-set and 10% test-set, the same as Fashionista. |
| Hardware Specification | Yes | We implement our architecture by using the Caffe [Jia et al., 2014] toolbox and NVIDIA TITAN X GPU to train the network. |
| Software Dependencies | No | The paper mentions using 'Caffe [Jia et al., 2014] toolbox' but does not specify its version number or any other software dependencies with specific versions. |
| Experiment Setup | Yes | The networks are trained in the CFPD and Fashionista training dataset with Adam optimization with β1 = 0.9 and β2 = 0.999. The learning rate is set to 1e-4, batch size is 1 with the consideration of GPU memory, and the weight decay is 0.0005. The reconstruction loss with the balance parameter λ = 0.125... We need about 100K training iterations for convergence. |