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..
iFAN: Image-Instance Full Alignment Networks for Adaptive Object Detection
Authors: Chenfan Zhuang, Xintong Han, Weilin Huang, Matthew Scott13122-13129
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate i FAN on two domain adaptation scenarios: 1) train on SIM10K (Johnson-Roberson et al. 2017) and test on Cityscapes (Cordts et al. 2016) dataset (SIM10K Cityscapes); 2) train on Cityscapes (Cordts et al. 2016) and test on Foggy Cityscapes (Sakaridis, Dai, and Van Gool 2018) (Cityscapes Foggy). ... Our method is compared with state-of-the-art UDA object detectors in Table 1. ... Ablation Study. We conduct ablation study by isolating each component in i FAN. The results are presented in Table 2 and 3. |
| Researcher Affiliation | Industry | Chenfan Zhuang, Xintong Han, Weilin Huang, Matthew R. Scott Malong Technologies, Shenzhen, China Shenzhen Malong Artificial Intelligence Research Center, Shenzhen, China EMAIL |
| Pseudocode | No | The paper does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is publicly available, nor does it provide a link. |
| Open Datasets | Yes | We evaluate i FAN on two domain adaptation scenarios: 1) train on SIM10K (Johnson-Roberson et al. 2017) and test on Cityscapes (Cordts et al. 2016) dataset (SIM10K Cityscapes); 2) train on Cityscapes (Cordts et al. 2016) and test on Foggy Cityscapes (Sakaridis, Dai, and Van Gool 2018) (Cityscapes Foggy). |
| Dataset Splits | Yes | The Cityscapes dataset has 3,475 images of 8 object categories taken from real urban scenes, where 2,975 images are used for training and the remaining 500 for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU or CPU models, memory sizes) used for the experiments. |
| Software Dependencies | No | The paper mentions 'implement all with maskrcnn-benchmark (Massa and Girshick 2018)' but does not specify software versions for PyTorch, Python, or other key libraries. |
| Experiment Setup | Yes | The shorter side of training and test images are set to 600. The detector is first trained with a learning rate of lr = 0.001 for 50K iterations, and then lr = 0.0001 for another 20K iterations. The category-agnostic/aware instance-level alignment late launches at 30K-th iteration and categorycorrelation alignment at 50K-th. ... We set λadv = 0.1 in Eqn. 7 and λl = 1.0 in Eqn. 3. The embedding dimension of category-correlation alignment is set to 256, with a margin of m = 1.0. VGG-16 is used as the backbone if not specifically indicated. |