most citedPaying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

1.6k citations · 1.6k across the 5 of their papers we have counts for

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5 papers

cs.CV20162 cited

Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling

Spyros Gidaris, Nikos Komodakis

Pixel wise image labeling is an interesting and challenging problem with great significance in the computer vision community. In order for a dense labeling algorithm to be able to…

cs.CV20161.6k cited

Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

Sergey Zagoruyko, Nikos Komodakis

Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying…

cs.CV201626 cited

A Deep Metric for Multimodal Registration

Martin Simonovsky, Benjamín Gutiérrez-Becker, Diana Mateus +2

Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities. The crucial component here is…

cs.CV20143 cited

Speeding-up Graphical Model Optimization via a Coarse-to-fine Cascade of Pruning Classifiers

B. Conejo, N. Komodakis, S. Leprince +1

We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach relie…

cs.CV20145 cited

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht +10

Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov Random Fields (MRF). This study provided valuable insights in choosing the best opt…