Memorability of Image Regions
By Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva. 2012
127 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Dacheng Tao
- Jitendra Malik
- Xiaogang Wang
- Antonio Torralba
- Ming-Hsuan Yang
- Andrea Vedaldi
Citations in the corpus
- COCO-Stuff: Thing and Stuff Classes in Context (2016, background): “rform significantly above chance at 59.1%, 60.2% and 67.8%, respectively. The relative image position cue performs suprisingly well at 87.7%, confirming the positional bias observed in many prior works [32,52,61,28,4,9]. The more complex objectness measure performs best at 90.6%. Using all cues we ach”
- Understanding and Predicting Image Memorability at a Large Scale (2015, methodology): “Indeed, computer vision works [12, 18, 15, 7] have been able to reliably estimate the memorability ranks of novel pictures, or faces, accounting for half of the variance in human consistency.”
- Modifying the Memorability of Face Photographs (2013, methodology): “Image memorability: Recent work in computer vision [11, 12, 14] has shown that picture and face memorability are largely intrinsic to the image and importantly reproducible across people from diverse backgrounds, meaning that most of us will tend to remember and forget the same images.”
- Adopting Abstract Images for Semantic Scene Understanding (2016, background): “Studying the contribution of different semantic features (e.g. presence, locations, attributes, cooccurrences of objects, etc.) to memorability would go a long way in understanding memorability.”
- What makes an image popular? (2014, methodology): “To better understand what makes an image popular, we attempt to attribute the popularity of an image to its re-873 gions (similar to [29]).”
- What Makes a Photograph Memorable? (2014, background): “have recently shown that they can generate similar memorability maps automatically from unlabeled images [37].”