Tap and Shoot Segmentation
Authors: Ding-Jie Chen, Jui-Ting Chien, Hwann-Tzong Chen, Long-Wen Chang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on various datasets show that, by training a deep convolutional network to integrate the selection and focus/defocus cues, our method can achieve higher segmentation accuracy in comparison with existing interactive segmentation methods. |
| Researcher Affiliation | Academia | Ding-Jie Chen, Jui-Ting Chien, Hwann-Tzong Chen, Long-Wen Chang National Tsing Hua University, Taiwan {djchen.tw, ydnaandy123}@gmail.com , {htchen, lchang}@cs.nthu.edu.tw |
| Pseudocode | No | The paper describes the network architecture and training process in text and diagrams (Figure 2), but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper provides links to code for *other* interactive segmentation algorithms (e.g., Grab Cut, Random Walks) but does not provide a link or statement about open-sourcing its own code. |
| Open Datasets | Yes | We evaluate all algorithms on four public datasets. Each image contains one foreground region with pixel-level ground-truth labeling. Grab Cut dataset (Rother, Kolmogorov, and Blake 2004): It contains 50 natural images. Berkeley dataset (Mc Guinness and O Connor 2010): It contains 100 images. The images are from the popular Berkeley dataset (Martin et al. 2001). Extended complex scene saliency dataset (ECSSD) (Shi et al. 2016): The dataset contains 1,000 natural images. MSRA10K dataset (Cheng et al. 2015a): This dataset contains 10,000 natural images. |
| Dataset Splits | Yes | We partition the dataset into three nonoverlapping subsets with the numbers of 8,000, 1,000, and 1,000 for training, validation, and testing. |
| Hardware Specification | Yes | All algorithms are run on the same environment (Intel i7-4770 3.40 GHz CPU, 8GB RAM, NVIDIA Titan X GPU). |
| Software Dependencies | No | The paper mentions 'We implement all of them in Tensorflow' but does not specify the version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | All models are optimized using ADAM algorithm with same learning rate 0.0001. The batch size is 9 and the network is running on Titan X. We also apply dropout layers on the layers Deconv8 to Deconv5 for avoiding over-fitting. The dropout probability is 0.2 during training and 0.0 during testing. |