DenserNet: Weakly Supervised Visual Localization Using Multi-Scale Feature Aggregation
Authors: Dongfang Liu, Yiming Cui, Liqi Yan, Christos Mousas, Baijian Yang, Yingjie Chen6101-6109
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results indicate that our method sets a new state-of-the-art on four challenging large-scale localization benchmarks and three image retrieval benchmarks with the same level of supervision. |
| Researcher Affiliation | Academia | 1Purdue University 2University of Florida 3Fudan University |
| Pseudocode | No | The architecture of Denser Net is shown in Figure 2. Denser Net includes a CNN backbone, three feature extraction branches, and a one-stage feature decoder. |
| Open Source Code | Yes | The code is available at https://github.com/goodproj13/ Denser Net. |
| Open Datasets | Yes | We train the proposed method by using Pitts30k-training dataset (Arandjelovic et al. 2016). Following (Arandjelovic et al. 2016; Jin Kim, Dunn, and Frahm 2017), we group the positive {I+ r } and negative {I r } images for each training query image It. |
| Dataset Splits | Yes | The 30K training query images generate four image triplets. Thus, we obtain a total of 120K image triplets with 112K for training and 8K for validation. |
| Hardware Specification | Yes | All experiments are performed on a workstation with an Intel Core i7-7820X CPU and four NVIDIA Ge Force GTX 3080Ti GPU. |
| Software Dependencies | No | Both VGG16 and Mobile Net V2 based methods are pretrained on Image Net (Deng et al. 2009). In training, we exploit standard data augmentation in training, such as motion blur, random Gaussian noise, brightness changes to improve the robustness of our methods to illumination variations and viewpoint changes. Specifically, the margin M is set at 0.1, 30 epochs are performed using batch size of 4 triplets, Adam (Kingma and Ba 2014) with the learning rates of 10 3 which is halved every 6 epochs, momentum of 0.9, and weight decay of 10 3. |
| Experiment Setup | Yes | Specifically, the margin M is set at 0.1, 30 epochs are performed using batch size of 4 triplets, Adam (Kingma and Ba 2014) with the learning rates of 10 3 which is halved every 6 epochs, momentum of 0.9, and weight decay of 10 3. |