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..
Quantized Correlation Hashing for Fast Cross-Modal Search
Authors: Botong Wu, Qiang Yang, Wei-Shi Zheng, Yizhou Wang, Jingdong Wang
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three real world datasets demonstrate that our approach outperforms the state-of-the-art multi-modal hashing methods. |
| Researcher Affiliation | Collaboration | School of Information Science and Technology, Sun Yat-sen University, China Nat l Eng. Lab. for Video Technology, Cooperative Medianet Innovation Center, Sch l of EECS, Peking University Collaborative Innovation Center of High Performance Computing, National University of Defense Technology Guangdong Provincial Key Laboratory of Computational Science Microsoft Research Asia, China |
| Pseudocode | Yes | Algorithm 1 Quantized Correlation Hashing |
| Open Source Code | No | The paper does not provide any explicit statement or link for the open-source availability of its code. |
| Open Datasets | Yes | To verify the efficiency and effectiveness of QCH, a series of experiments are carried out on two benchmark multimodal datasets, Wiki[Rasiwasia et al., 2010] and NUS-WIDE [Chua et al., 2009], and a large-scale dataset 58W-CIFAR [Krizhevsky and Hinton, 2009] for which we extracted two types of features to build multi-view data, so that cross-view retrieval can be performed. |
| Dataset Splits | No | The paper specifies training and testing sets, but does not explicitly mention a separate validation set or split for model tuning. |
| Hardware Specification | Yes | All the experiments were conducted on a workstation with 24 Intel(R) Xeon(R) E5-2620@2.0GHz CPUs, 96 GB RAM and 64-bit Ubuntu system. |
| Software Dependencies | No | The paper mentions a "64-bit Ubuntu system" but does not specify any software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | Firstly, we investigate the influence of two parameters introduced in QCH: α and β. α controls the tradeoff between hash function learning stage and quantization stage and β is a regularizer coefficient. During this experiment, c = 16 is used. [...] setting α = 0.05 and β = 0.02 is a reasonable to QCH for Wiki and NUS-WIDE datasets. QCH performs better on 58W-CIFAR dataset when α and β are larger. Since multi-view data have stronger correlation than cross-modal data, so we set larger values, i.e. α = 1 and β = 0.1 on multi-view datasets for example 58W-CIFAR. |