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..
One-Shot Object Detection with Co-Attention and Co-Excitation
Authors: Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train and evaluate our model on VOC and COCO benchmark datasets. For VOC, our model is trained on the union set of VOC 2007 train&val sets and VOC 2012 train&val sets, and is evaluated on VOC 2007 test set. ... Table 1 shows that our model using reduced Image Net pre-trained backbone ( Ours (725) ) still achieves better performance on both seen and unseen classes than the baseline methods. |
| Researcher Affiliation | Collaboration | 1National Tsing Hua University, 2Academia Sinica, Taiwan, 3Aeolus Robotics, 4Taiwan AI Labs |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Codes are available at https://github.com/timy90022/One-Shot-Object-Detection. |
| Open Datasets | Yes | Following the previous work [22, 24], we train and evaluate our model on VOC and COCO benchmark datasets. |
| Dataset Splits | Yes | For VOC, our model is trained on the union set of VOC 2007 train&val sets and VOC 2012 train&val sets, and is evaluated on VOC 2007 test set. For COCO, our model is trained on COCO train 2017 set and evaluated on COCO val 2017 set. |
| Hardware Specification | Yes | We train our models using SGD optimizer with momentum 0.9 for ten epochs, with batch size 128 on eight NVIDIA V100 GPUs in parallel. |
| Software Dependencies | No | The paper mentions using Mask-RCNN and provides a link to an implementation, but it does not specify software dependencies with version numbers for its own method (e.g., Python, PyTorch/TensorFlow versions, or other libraries with versions). |
| Experiment Setup | Yes | We train our models using SGD optimizer with momentum 0.9 for ten epochs, with batch size 128 on eight NVIDIA V100 GPUs in parallel. We use a learning rate starting with 0.01, and then decay it by a ratio 0.1 for every four epochs. We use λ = 3 in (7) for the margin-based ranking loss. |