Deep Object Co-Segmentation via Spatial-Semantic Network Modulation
Authors: Kaihua Zhang, Jin Chen, Bo Liu, Qingshan Liu12813-12820
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four image co-segmentation benchmark datasets demonstrate the superior accuracy of the proposed method compared to state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Kaihua Zhang,1 Jin Chen,1 Bo Liu,2 Qingshan Liu1 1B-DAT and CICAEET, Nanjing University of Information Science and Technology, Nanjing, China 2JD Finance America Corporation |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are available at http://kaihuazhang.net/. |
| Open Datasets | Yes | We conduct extensive evaluations on four widely-used benchmark datasets (Faktor and Irani 2013; Rubinstein et al. 2013) including sub-set of MSRC, Internet, sub-set of i Coseg, and PASCAL-VOC. ... We adopt the COCO-SEG dataset released by (Wang et al. 2019) to train our model. |
| Dataset Splits | No | The paper mentions using several datasets for evaluation, including COCO-SEG for training, but does not explicitly state the specific train/validation/test splits (e.g., percentages or sample counts) used for these datasets within the paper. |
| Hardware Specification | Yes | Our model is implemented in Py Torch and a Nvidia RTX 2080Ti GPU is adopted for acceleration. |
| Software Dependencies | No | The paper states the model is 'implemented in Py Torch' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We follow the same settings as (Wei et al. 2017; Wang et al. 2019): the input image group I consists of N = 5 images that are randomly selected from a group of images with co-object category, and a mini-batch of 4 I is fed into the model simultaneously during training. All images in I are resized to 224 224 as input... We leverage the Adam algorithm (Kingma and Ba 2014) to optimize the whole network in an end-to-end manner, among which the exponential decay rates for estimating the first and the second moments are set to 0.9 and 0.999, respectively. The learning rate starts from 1e-4 and reduces by a half every 25, 000 steps until the model converges at about 200,000 steps. |