CoSegNet: Image Co-segmentation using a Conditional Siamese Convolutional Network
Authors: Sayan Banerjee, Avik Hati, Subhasis Chaudhuri, Rajbabu Velmurugan
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results reflect an excellent performance of our method compared to stateof-the-art methods on challenging co-segmentation datasets. We evaluate the proposed method on three challenging cosegmentation datasets Pascal-VOC, Internet and MSRC and compare with state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Indian Institute of Technology Bombay, India 2Istituto Italiano di Tecnologia,Genova, Italy |
| Pseudocode | No | The paper describes the network architecture and training process in text and diagrams, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement providing concrete access to source code for the methodology described, nor does it include a link to a repository or mention supplementary materials for code. |
| Open Datasets | Yes | We evaluate the proposed method on three challenging cosegmentation datasets Pascal-VOC, Internet and MSRC and compare with state-of-the-art methods. referencing [Everingham et al., 2010] for PASCAL-VOC and [Rubinstein et al., 2013] for Internet and MSRC. |
| Dataset Splits | Yes | For Pascal-VOC and MSRC datasets, we randomly split the dataset in the ratio of 3:1:1 for training, validation and testing sets. |
| Hardware Specification | No | The paper mentions memory constraints influencing batch size ('Due to memory constraint, we use a batch size of 3') but does not provide any specific details about the hardware (e.g., GPU, CPU models, memory size) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific details about ancillary software dependencies, such as programming languages, libraries, or frameworks, along with their version numbers. |
| Experiment Setup | Yes | We use stochastic gradient descent as our optimizer and fix the learning rate and momentum at 0.00001 and 0.9, respectively for all three datasets. For Pascal-VOC and MSRC datasets, we set the weight decay to 0.0004 and for Internet we set it to 0.0005. Due to memory constraint, we use a batch size of 3. We resize all the input images to 224 224 and set the margin α to 1. |