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..
Dependency Exploitation: A Unified CNN-RNN Approach for Visual Emotion Recognition
Authors: Xinge Zhu, Liang Li, Weigang Zhang, Tianrong Rao, Min Xu, Qingming Huang, Dong Xu
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both Internet images and art photo datasets demonstrate that our method outperforms the state-of-the-art methods with at least 7% performance improvement. |
| Researcher Affiliation | Academia | University of Chinese Academy of Sciences, China; Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS, China; Harbin Institute of Technology, Weihai, China; University of Technology, Sydney, Australia; University of Sydney, Australia |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our model and results are available online1. 1https://github.com/WERush/Unified_CNN_RNN |
| Open Datasets | Yes | The large scale emotion dataset is recently published in [You et al., 2016]... We use the labeled dataset and the same training/testing split as in [Rao et al., 2016b] to evaluate these methods. ... The Art Photo dataset [Machajdik and Hanbury, 2010]... In [Mikels et al., 2005], 395 images are collected from the standard IAPS dataset and labeled with arousal and valence values, which formed the IAPS-Subset dataset. |
| Dataset Splits | Yes | Specifically, the dataset is randomly split into a training set (80%, 18,532 images), a testing set (15%, 3,474 images) and a validation set (5%, 1,158 images). |
| Hardware Specification | Yes | Our model is implemented by using Torch7 [Collobert et al., 2011] on one Nvidia GTX Titan X. |
| Software Dependencies | No | The paper mentions "Torch7" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set λ = 0.5 in E.q (11) to balance the loss function and the regularization term, and set margin µ = 1. The batch size is set to 64, and the CNN part is optimized by using the SGD with learning rate = 0.001 and Bi GRU is optimized by using Rmsprop [Tieleman and Hinton, 2012] with the learning rate as 0.0001. In addition, a staircase weight decay is applied after 10 epoches. |