Visual Sentiment Analysis by Attending on Local Image Regions
Authors: Quanzeng You, Hailin Jin, Jiebo Luo
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results suggest that 1) our model is capable of automatically discovering sentimental local regions of given images and 2) it outperforms existing state-of-the-art algorithms to visual sentiment analysis. |
| Researcher Affiliation | Collaboration | Quanzeng You Department of Computer Science University of Rochester Rochester, NY 14627 qyou@cs.rochester.edu Hailin Jin Adobe Research 345 Park Avenue San Jose, CA 95110 hljin@adobe.com Jiebo Luo Department of Computer Science University of Rochester Rochester, NY 14627 jluo@cs.rochester.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to or statements about the availability of its own source code for the described methodology. |
| Open Datasets | Yes | We evaluate the proposed model on the publicly available benchmark dataset visual sentiment ontology (VSO)... 1http://www.ee.columbia.edu/ln/dvmm/vso/download/sentibank.html |
| Dataset Splits | Yes | We randomly split them into 80% for training, 10% for testing and 10% for validating. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Caffe" but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | All the parameters are automatically learned by minimizing the two loss functions over the training split. We use a mini-batch gradient descent algorithm with an adaptive learning rate to optimize the loss functions. After only 2 epochs over the training split, the classification on validating split has been all correct. |