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 [1].
Visual Sentiment Analysis by Attending on Local Image Regions
Authors: Quanzeng You, Hailin Jin, Jiebo Luo
AAAI 2017 | Venue PDF | 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 EMAIL Hailin Jin Adobe Research 345 Park Avenue San Jose, CA 95110 EMAIL Jiebo Luo Department of Computer Science University of Rochester Rochester, NY 14627 EMAIL |
| 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. |