Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Graph Convolutional Network Hashing for Cross-Modal Retrieval
Authors: Ruiqing Xu, Chao Li, Junchi Yan, Cheng Deng, Xianglong Liu
IJCAI 2019 | Venue PDF | 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. |