Detecting Robust Co-Saliency with Recurrent Co-Attention Neural Network
Authors: Bo Li, Zhengxing Sun, Lv Tang, Yunhan Sun, Jinlong Shi
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China 2Jiangsu University of Science and Technology Zhenjiang, China |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | Yes | Datasets We evaluated the proposed approach on three public benchmark datasets: i Coseg [Batra et al., 2010], MSRC [Winn et al., 2005] and Cosal2015 [Zhang et al., 2016]. ...We select the widely used pre-trained VGG 16-layer net [Simonyan and Zisserman, 2014] (over the MS COCO dataset [Lin et al., 2014])... |
| Dataset Splits | No | For the sake of fair comparison, we following the same setting in [Wei et al., 2017; Zheng et al., 2018], the training groups are randomly selected from a subset of COCO dataset (which has 9213 images with pixelwised ground-truth) using the global similarity (Gist and Lab color histogram features), and then fine-tune the model by randomly selecting 50% 50% training-test images for three datasets. The paper mentions training and testing splits but does not explicitly define a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'pre-trained VGG 16-layer net' and optimization by 'standard SGD', but does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | The proposed models are optimized by standard SGD in which the momentum parameter is chosen as 0.99, the learning rate is set to 1e-5, and the weight decay is 0.0005. We need about 50000 training iterations for convergence. And for co-perceptual loss, we follow the setting in Neural Style Transfer and use the activated layers Relu3 1, Relu4 1, Relu5 1 of VGG as the hidden representation. And the loss tradeoff parameter λ is set to be 0.1 in our work. |