Graph Convolutional Network Hashing for Cross-Modal Retrieval
Authors: Ruiqing Xu, Chao Li, Junchi Yan, Cheng Deng, Xianglong Liu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets demonstrate that the proposed GCH outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1School of Electronic Engineering, Xidian University 2Dept. of CSE & Mo E Key Lab of Artificial Intelligence, Shanghai Jiao Tong University 3Beihang University |
| Pseudocode | Yes | Algorithm 1 Semantic encoder guided learning for graph convolutional network hashing (GCH). |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Three popular benchmark datasets in cross-modal retrieval: MIRFLICKR-25K [Huiskes and Lew, 2008], NUS-WIDE [Chua et al., 2009], and Microsoft COCO [Lin et al., 2014] are adopted to validate our proposed method. |
| Dataset Splits | No | For MIRFLICKR-25K: 'using 10,000 data points for training and 2,000 for query. The remaining part is used for retrieval.' For NUS-WIDE: '10,500 data points for training and 2,100 data points for query. The rest serves as retrieval set.' For MS-COCO: '10,000 data points for training and 5,000 for query. The rest of data points serve as retrieval set.' While MS-COCO dataset itself has a 'validation' split mentioned, the paper's specific experimental split uses 'training' and 'query' (test) sets, but doesn't explicitly state a validation split for their own process. |
| Hardware Specification | Yes | Our GCH is implemented with Tensor Flow [Abadi et al., 2016] and executed on a server with one NVIDIA TITAN Xp GPU. |
| Software Dependencies | No | Our GCH is implemented with Tensor Flow [Abadi et al., 2016]. No specific version number is provided for TensorFlow or any other software dependency. |
| Experiment Setup | Yes | Initialization: network parameters θx,y,l,G; hyperparameters: α, β, γ; learning rate: µ; mini-batch size: N x,y,l b = 128; maximum iteration number: Tmax, iter=0; We adopt the first seven layers of CNNF [Chatfield et al., 2014] neural network as image feature encoder... For texts, a neural network with four fully-connected layers is constructed... Semantic encoder is built with a three-layer feed-forward network... A two-layer GCN with each layer s output feature dimensions being Nb 1024 and Nb K is employed... Sigmoid activation is used to output predicted labels; tanh activation is used to output hash codes; and the rest of the layers are all uniformly activated by the LRe LU function. |