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..
Deep Multiple Instance Hashing for Object-based Image Retrieval
Authors: Wanqing Zhao, Ziyu Guan, Hangzai Luo, Jinye Peng, Jianping Fan
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three benchmark datasets demonstrate the learned hash codes well preserve the object-level similarity and DMIH outperforms baselines on both single-object and multi-object queries. |
| Researcher Affiliation | Academia | Wanqing Zhao, Ziyu Guan , Hangzai Luo, Jinye Peng School of information and technology Northwestern University, Shaanxi, China EMAIL Jianping Fan Department of Computer Science UNC-Charlotte, NC28223, USA EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the methodology described. |
| Open Datasets | Yes | SIVAL. It is a benchmark dataset that emphasizes the task of object-based image retrieval. ... Pascal VOC 2007 [Everingham et al., 2007]. It contains 9,963 images with 20 different object categories. ... ILSVRC 2013 detection set. This dataset has a similar task and style with PASCAL VOC, but contains more images and categories. |
| Dataset Splits | No | The paper mentions training and testing sets, but does not specify a distinct validation dataset split. It mentions 'cross validation' for hyper-parameter tuning but not a data split for validation. |
| Hardware Specification | Yes | All the methods are run on a PC with NVIDIA GTX 1070 GPU, Inter Core i7-7700 CPU and 16GB memory. |
| Software Dependencies | No | The paper mentions using VGG-16 as a base network, but does not provide specific version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | The hyper-parameter λ in Eq. (7) controls the balance between the two task losses. β and λ are set to 1.25 and 1 respectively by cross validation. ... We empirically set the objectness threshold θ = 0.7 for DMIH and its variants. Parameters of Pm H and DSRH are set to the best values reported. |