Weakly Supervised Local-Global Relation Network for Facial Expression Recognition
Authors: Haifeng Zhang, Wen Su, Jun Yu, Zengfu Wang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on lab-controlled and real-world facial expression dataset show that WS-LGRN achieves state-of-the-art performance, which demonstrates its superiority in FER. |
| Researcher Affiliation | Academia | Haifeng Zhang1 , Wen Su3 , Jun Yu1 and Zengfu Wang1,2 1Department of Automation, University of Science and Technology of China 2Institute of Intelligent Machines, Chinese Academy of Sciences 3Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University |
| Pseudocode | No | The paper describes the proposed framework and its components using textual descriptions and architectural diagrams (e.g., Figure 2 and Figure 5), but it does not contain any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | Yes | Most of our experiments are conducted on the CK+ [Lucey et al., 2010] dataset. |
| Dataset Splits | Yes | We randomly select 30,000 (The ratio of positive and negative samples is 1:1) images to train the eyes-related branch and select 3,000 images for validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions general components like 'a variant of Densenet' for the backbone and 'Stochastic Gradient Descent' for optimization, but it does not specify any software names with version numbers (e.g., 'Python 3.x', 'PyTorch 1.x', 'TensorFlow 2.x') for reproducibility. |
| Experiment Setup | Yes | The initial learning rate is set to 0.1, which is decreased by 0.1 after every 20 epochs. |