Learning where to look

By B. Ehret. 1999

How researchers see this paper

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

DRAW: A Recurrent Neural Network For Image Generation (2015, background): “…structure can be better captured by a sequence of partial glimpses, or foveations, than by a single sweep through the entire image (Larochelle & Hinton, 2010; Denil et al., 2012; Tang et al., 2013; Ranzato, 2014; Zheng et al., 2014; Mnih et al., 2014; Ba et al., 2014; Ser-manet et al., 2014).”
Recurrent Models of Visual Attention (2014, background): “Our work is perhaps the most similar to the other attempts to implement attentional processing in a deep learning framework [6, 14, 17].”
Learning Generative Models with Visual Attention (2013, background): “Several other related models attempt to learn where to look for objects [19, 20] and for video based tracking [21].”
Attention for Fine-Grained Categorization (2014, methodology): “…simple shapes (Schmidhuber & Huber, 1991), tasks based on MNIST digits (Larochelle & Hinton, 2010; Bazzani et al., 2011; Denil et al., 2012; Ranzato, 2014; Mnih et al., 2014), the vision-control game of “catch” (Mnih et al., 2014), expression classiﬁcation for 100 × 100 aligned faces…”
Seeing with Humans: Gaze-Assisted Neural Image Captioning (2016, background): “Attention mechanisms were shown to reduce computation and to make the networks more robust to changes in input resolution [16], [17], [36].”