Approximating Discrete Probability Distribution of Image Emotions by Multi-Modal Features Fusion
Authors: Sicheng Zhao, Guiguang Ding, Yue Gao, Jungong Han
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three datasets verify the superiority of the proposed method, as compared to the state-of-the-art. |
| Researcher Affiliation | Academia | Sicheng Zhao , Guiguang Ding , Yue Gao Jungong Han School of Software, Tsinghua University, Beijing 100084, China School of Computing & Communications, Lancaster University, UK schzhao@gmail.com, {dinggg,gaoyue}@tsinghua.edu.cn, jungonghan77@gmail.com |
| Pseudocode | Yes | Algorithm 1: Procedure for weighted multi-modal shared sparse leaning |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | To our knowledge, there are three public datasets that contain DPD information of image emotions: Abstract [Machajdik and Hanbury, 2010], Emotion6 [Peng et al., 2015] and Image Emotion-Social-Net (IESN) [Zhao et al., 2016]. |
| Dataset Splits | No | The paper states: "We randomly select 80%, 50% and 50% of images from the Abstract, Emotion6 and IESN datasets respectively as the training set and the remained form the testing set." It specifies training and testing sets, but does not mention a validation set. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions "Caffe reference model" but does not specify any version numbers for Caffe or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | The following parameter settings are adopted for WMMSSL: α = 0.05 and β = 0.1. |