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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Approximating Discrete Probability Distribution of Image Emotions by Multi-Modal Features Fusion
Authors: Sicheng Zhao, Guiguang Ding, Yue Gao, Jungong Han
IJCAI 2017 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |