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20152022
most citedSimple vs complex temporal recurrences for video saliency prediction

26 citations · 36 across the 6 of their papers we have counts for

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cs.CV2022

QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

David Berga, Pau Gallés, Katalin Takáts +5

Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Obs…

cs.CV20201 cited

Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates

Feiyan Hu, Eva Mohedano, Noel O'Connor +1

Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources. This paper a…

cs.CV201926 cited

Simple vs complex temporal recurrences for video saliency prediction

Panagiotis Linardos, Eva Mohedano, Juan Jose Nieto +3

This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal…

cs.CV2019

An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval

Federico Magliani, Kevin McGuinness, Eva Mohedano +1

The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance. One of the main bottlene…

cs.CV2018

Temporal Saliency Adaptation in Egocentric Videos

Panagiotis Linardos, Eva Mohedano, Monica Cherto +2

This work adapts a deep neural model for image saliency prediction to the temporal domain of egocentric video. We compute the saliency map for each video frame, firstly with an off…

cs.CV2017

Saliency Weighted Convolutional Features for Instance Search

Eva Mohedano, Kevin McGuinness, Xavier Giro-i-Nieto +1

This work explores attention models to weight the contribution of local convolutional representations for the instance search task. We present a retrieval framework based on bags o…