Scanpath Prediction for Visual Attention using IOR-ROI LSTM
Authors: Zhenzhong Chen, Wanjie Sun
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that the proposed architecture can achieve superior performance in predicting scanpaths. Results on the OSIE dataset [Xu et al., 2014] and MIT dataset [Judd et al., 2009] have verified the effectiveness of our model. We trained and evaluated our proposed model on the widely used public OSIE and MIT eye-tracking datasets. |
| Researcher Affiliation | Academia | Zhenzhong Chen, Wanjie Sun School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, P.R. China |
| Pseudocode | No | The paper describes the model architecture and equations but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We trained and evaluated our proposed model on the widely used public OSIE and MIT eye-tracking datasets consisting of natural images along with eye-tracking data from different participants. In the OSIE dataset, there are 700 images with 800 600 pixels. Eye-tracking data was acquired from 15 participants for each image. The MIT dataset is composed of 1003 images with resolution ranging from 405 to 1024. Eye-tracking data was also recorded from 15 subjects for each image. |
| Dataset Splits | Yes | We randomly splitted the OSIE and MIT datasets into 80% training data and 20% test data. |
| Hardware Specification | No | The paper mentions implementing parts "on the modern GPU" and using a "pre-trained VGG-19 network" but does not specify any exact GPU or CPU models, memory sizes, or other detailed hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions various components like Conv LSTM, VGG-19, Adam optimizer, and ReLU activation functions but does not specify any software names with version numbers (e.g., Python, TensorFlow, PyTorch versions, specific libraries). |
| Experiment Setup | Yes | The model was trained using the Adam optimizer along with step learning rate decay strategy which decays learning rate at a constant rate of 0.9. Initial learning rate for the IORROI LSTM and saliency guidance part is 1 10 4 while fine-tuning the dilated VGG-19 with an initial learning rate of 1 10 5. |