Learning deep structured network for weakly supervised change detection
Authors: Salman Khan, Xuming He, Fatih Porikli, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluations on four benchmark datasets demonstrate superior detection and localization performance. [...] 4 Experiments [...] Our experimental results demonstrate that the proposed approach outperforms the state-of-the-art by a large margin (Sec. 4.3). The key contributions of our work include: [...] Furthermore, we perform a rigorous evaluation on three other relevant datasets. |
| Researcher Affiliation | Academia | Salman Khan1,2 , Xuming He3 , Fatih Porikli2, Mohammed Bennamoun4 Ferdous Sohel5 and Roberto Togneri4 1Data61-CSIRO, Canberra, Australia 2Australian National University, Canberra, Australia 3Shanghai Tech University, Shanghai, China 4The University of Western Australia, Perth, Australia 5Murdoch University, Perth, Australia |
| Pseudocode | No | The paper describes algorithms but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate our method on the following four datasets. [...] CDnet 2014 Dataset: [Wang et al., 2014] [...] AICD 2012 Dataset: Aerial Image Change Detection (AICD) dataset [Bourdis et al., 2011] [...] GASI 2015 Dataset: Geoscience Australia Satellite Image (GASI) dataset is a custom built dataset based on the changes occurred during 1999 2015 in a 100 100 km2 area in the east of city of Melbourne in Victoria, Australia [Khan et al., 2017]. [...] PCD 2015 Dataset: Panoramic change detection dataset [Sakurada and Okatani, 2015] |
| Dataset Splits | Yes | For each test image pair, we find K closely matching pairs from the training set using a KNN search and average their foreground label proportion to estimate τ (details in Sec. 4.3). [...] Note that for the training, we validate a fixed-value τ, which is faster than using KNN. As the pixel-level labels are unavailable in training, we set τ to a value which gives coverage of at least 15% of each image on a validation set. |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications (e.g., specific GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'lib-linear package' and 'Caffe', and refers to network architectures like 'VGGnet' and 'FCN-VGG16-16s', but it does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The network weights are initialized from a pre-trained VGG network (on Image Net). [...] enlarged paired images of size 512x512 are fed to the CNN. Moreover, the convolution filter size in FC1 (segmentation branch) is kept to 1x1 (in contrast to a 7x7 filter size in FC1 ) to avoid the additional decrease in resolution of the 32x32 output map. [...] As the pixel-level labels are unavailable in training, we set τ to a value which gives coverage of at least 15% of each image on a validation set. |