Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Learning to Rank for Content-Based Image Retrieval
Authors: Ji Wan, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Xingyu Gao, Dayong Wang, Yongdong Zhang, Jintao Li
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,108 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,988 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an extensive set of experiments, in which encouraging results show that our technique is effective, scalable and promising for large-scale CBIR. |
| Researcher Affiliation | Academia | Ji Wan1,2,3, Pengcheng Wu2, Steven C. H. Hoi2, Peilin Zhao4, Xingyu Gao1,2,3, Dayong Wang5, Yongdong Zhang1, Jintao Li1 1 Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS, China 2 Singapore Management University 3 University of the Chinese Academy of Sciences 4 Institute for Infocomm Research, A*STAR, Singapore 5 Michigan State University, MI, USA |
| Pseudocode | No | The paper describes algorithms (OPR, OPAR, OGDR) using mathematical equations and textual descriptions but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement or link was provided indicating that the authors have made their source code for the methodology described in this paper publicly available. |
| Open Datasets | Yes | Table 1 shows a list of image databases in our testbed. ... Holiday, Caltech101, Image CLEF, Corel, Image CLEFFlickr |
| Dataset Splits | Yes | For each database, we randomly split it into five folds, in which one fold is used for test, one is for validation, and the rest are for training. and For validation and test data sets, we randomly choose 300 validation images and 150 test images from each fold. |
| Hardware Specification | No | The paper discusses 'CPU time cost' and running times but does not provide specific hardware details such as CPU/GPU models, memory, or processor types used for the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and machine learning techniques but does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, specific library versions). |
| Experiment Setup | No | The paper mentions that parameters were chosen via cross-validation ('To conduct a fair evaluation, we choose the parameters of different algorithms via the same cross validation scheme in all the experiments.') but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |