L-CoDe:Language-Based Colorization Using Color-Object Decoupled Conditions
Authors: Shuchen Weng, Hao Wu, Zheng Chang, Jiajun Tang, Si Li, Boxin Shi2677-2684
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach outperforms state-of-the-art methods conditioned on captions. |
| Researcher Affiliation | Academia | 1 School of Computer Science, Peking University 2 School of Software and Microelectronics, Peking University 3 School of Artificial Intelligence, Beijing University of Posts and Telecommunications 4 Institute for Artificial Intelligence, Peking University 5 Beijing Academy of Artificial Intelligence 6 Peng Cheng Laboratory |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states, "To make a fair comparison, we retrain these networks using their publicly available code on the reorganized dataset," referring to Manjunatha et al. (2018) and Xie (2018). However, it does not state that the code for L-Co De is publicly available, nor does it provide a link. |
| Open Datasets | Yes | Learning-based image colorization methods could be trained using the Image Net dataset (Russakovsky et al. 2015). However, language-based colorization requires a caption describing the grayscale image, so existing language-based methods choose to use image datasets with captions, such as the COCO-Stuff (Caesar, Uijlings, and Ferrari 2018). |
| Dataset Splits | Yes | According to Xie (2018), we keep the images whose captions contain adjectives and have 59K training images and 2.4K validation images left. |
| Hardware Specification | Yes | Experiments were conducted on two NVIDIA GTX 1080Ti GPUs. |
| Software Dependencies | No | The paper mentions software components like "Bi-LSTM" and "VGG16-BN" but does not specify their version numbers or the versions of any other software dependencies (e.g., Python, PyTorch). |
| Experiment Setup | Yes | We set the batch size to 16, α = 0.1 in the ATM. We minimize our objective loss using Adam optimizer with learning rate set as 2 10 4 and momentum parameters β1 = 0.99 and β2 = 0.999. |