Dynamically Visual Disambiguation of Keyword-based Image Search
Authors: Yazhou Yao, Zeren Sun, Fumin Shen, Li Liu, Limin Wang, Fan Zhu, Lizhong Ding, Gangshan Wu, Ling Shao
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our proposed approach. |
| Researcher Affiliation | Collaboration | 1Nanjing University of Science and Technology, Nanjing, China 2Inception Institute of Artificial Intelligence, Abu Dhabi, UAE 3University of Electronic Science and Technology of China, Chengdu, China 4Nanjing University, Nanjing, China |
| Pseudocode | No | The paper describes its methods through text and diagrams (Fig. 2 and Fig. 3) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing open-source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | Two widely used polysemy datasets CMU-Polysemy-30 [Chen, 2015] and MIT-ISD [Saenko, 2009] are employed to validate the proposed framework. |
| Dataset Splits | No | For the main model training and evaluation, the paper states, 'we exploit web images as the training set, human-labeled images in CMU-Polysemy-30 and MIT-ISD as the testing set.' While a split is mentioned for an intermediate step ('The collected 100 images for each selected text query were randomly split into a training set and testing set (e.g., Im = {It m = 50, Iv m = 50} and In = {It n = 50, Iv n = 50})'), a distinct validation set for the primary model training is not explicitly described. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'linear SVM classifier' and deep learning models like 'VGG-16' and 'Alex Net', but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | DMIL is trained for 100 epochs with an initial learning rate selected from [0.0001, 0.002]. In addition, parameters for text query selection are specified: 'α is selected from {0.2, 0.4, 0.5, 0.6, 0.8} and β is selected from {10, 20, 30, 40, 50} in (2).', 'γn is set γn 0.5 in (4).', 'The value of I(tq) is selected from {1, 2, 3, 4, 5, 6, 7, 8, 9}.', and 'N is selected from {10, 20, 30, 40, 50, 60}.' |