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 | Conference PDF | Archive PDF | Plain Text | 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. |