Equally-Guided Discriminative Hashing for Cross-modal Retrieval
Authors: Yufeng Shi, Xinge You, Feng Zheng, Shuo Wang, Qinmu Peng
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Experiments 3.1 Datasets Performance evaluation was conducted on two benchmark datasets: MIRFLICKR-25K [Huiskes and Lew, 2008] and MS COCO [Lin et al., 2014]. |
| Researcher Affiliation | Academia | 1School of Electronic Information and Communications, Huazhong University of Science and Technology 2Department of Computer Science and Engineering, Southern University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 Equally-Guided Discriminative Hashing |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their method is open-source or publicly available. |
| Open Datasets | Yes | Performance evaluation was conducted on two benchmark datasets: MIRFLICKR-25K [Huiskes and Lew, 2008] and MS COCO [Lin et al., 2014]. |
| Dataset Splits | No | For both datasets, 10000 image-text pairs are randomly chosen from retrieval set for training. The paper mentions the original MS COCO dataset has training and validation images, but does not specify a validation split created for *their* experiments. |
| Hardware Specification | Yes | We implement all deep learning methods with Tensorflow on a NVIDIA 1080ti GPU server. |
| Software Dependencies | No | We implement all deep learning methods with Tensorflow on a NVIDIA 1080ti GPU server. The paper mentions TensorFlow but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | We set hyper-parameters as: α = β = γ = 1. To learn neural network parameters, we apply the Adam solver with a learning rate within 10 2 10 6 and set batch size as 128. |