Adaptive Edge Attention for Graph Matching with Outliers
Authors: Jingwei Qu, Haibin Ling, Chenrui Zhang, Xiaoqing Lyu, Zhi Tang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that EAGM achieves promising matching quality compared with state-of-the-arts, on cases both with and without outliers. Our source code along with the experiments is available at https://github.com/bestwei/EAGM. |
| Researcher Affiliation | Academia | 1Wangxuan Institute of Computer Technology, Peking University, Beijing, China 2Department of Computer Science, Stony Brook University, Stony Brook, NY 11794 USA |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our source code along with the experiments is available at https://github.com/bestwei/EAGM. |
| Open Datasets | Yes | The experiments are performed on two cases with and without outliers, including three benchmarks for keypoint matching: Pascal VOC [Everingham et al., 2010] with Berkeley annotations [Bourdev and Malik, 2009], Willow Object [Cho et al., 2013], and CMU House Sequence [Caetano et al., 2006]. |
| Dataset Splits | No | The paper specifies training and testing sets but does not explicitly mention a separate validation set or its split details. |
| Hardware Specification | Yes | All experiments are run on a single GTX-1080Ti GPU, and around 25 image pairs are processed per second. |
| Software Dependencies | No | The paper mentions using ADAM optimizer, VGG16 network, and ImageNet, but does not provide specific version numbers for any software libraries or dependencies (e.g., Python version, PyTorch version, CUDA version). |
| Experiment Setup | Yes | For all experiments, optimization is achieved via ADAM optimizer [Kingma and Ba, 2015] with initial learning rate 1 × 10−3, and exponential decaying 2% per 2000 iterations. ... We empirically set the number of convolutional layers l1 = 3 and l2 = 10 in the edge attention module and classification module respectively. The weights in Eq. 12 are set as λe = λc = 0.1 during training. |