ReSeg: A Recurrent Neural Network for Object Segmentation

By Francesco Visin, Kyle Kastner, Aaron C. Courville, Yoshua Bengio, Matteo Matteucci, Kyunghyun Cho. 2015

39 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jiuxiang Gu
2. G. Wang
3. Jun Liu
4. Jianfei Cai
5. Amir Shahroudy
6. Yoshua Bengio
7. Bing Shuai
8. Hossein Rahmani

Citations in the corpus

• Recent advances in convolutional neural networks (2015, methodology): “During forward propagation process, λ is recorded and will be used for the backpropagation operation.”
• A guide to convolution arithmetic for deep learning (2016, methodology): “Finally, so-called transposed convolutional layers (also known as fractionally strided convolutional layers) have been employed in more and more work as of late (Zeiler et al., 2011; Zeiler and Fergus, 2014; Long et al., 2015; Radford et al., 2015; Visin et al., 2015; Im et al., 2016), and their rel”
• Spatial–Temporal Recurrent Neural Network for Emotion Recognition (2017, methodology): “For this reason, RNN is usually combined with CNN to model spatio-temporal information in some recognition tasks [22]–[24], [28], [29].”
• Episodic CAMN: Contextual Attention-Based Memory Networks with Iterative Feedback for Scene Labeling (2017, background): “Long-short Term Memory (LSTM) and Gated Recurrent Units (GRUs) [14]) are commonly inserted after convolution layers to explicitly capture and encode long-range context into local representations [5,51,59,66].”
• Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks (2017, background): “…networks that have been proposed in computer vision, e.g., fully connected network (Long et al., 2015), SegNet (Badrinarayanan et al., 2015), ReSeg (Visin et al., 2015), DeepLab (Chen et al., 2015; Papandreou et al., 2015), Deconvolutional network (Noh et al., 2015), Decoupled Network (Hong et…”
• Mollifying Networks (2016, background): “, 2014) – set the state of the art on a range of challenging tasks (Szegedy et al., 2014; Visin et al., 2015; Hinton et al., 2012; Sutskever et al., 2014; Bahdanau et al., 2014; Mnih et al., 2013; Silver et al., 2016).”