@conference {cLinardosa, title = {Simple vs complex temporal recurrences for video saliency prediction}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2019}, month = {09/2019}, publisher = {British Machine Vision Association}, organization = {British Machine Vision Association}, address = {Cardiff, Wales / UK.}, abstract = {

This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal domain. The first modification is the addition of a ConvLSTM within the architecture, while the second is a computationally simple exponential moving average of an internal convolutional state. We use weights pre-trained on the SALICON dataset and fine-tune our model on DHF1K. Our results show that both modifications achieve state-of-the-art results and produce similar saliency maps.

}, url = {https://bmvc2019.org/wp-content/uploads/papers/0952-paper.pdf}, author = {Linardos, Panagiotis and Mohedano, Eva and Nieto, Juan Jos{\'e} and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto and McGuinness, Kevin} } @conference {cMohedanob, title = {Saliency Weighted Convolutional Features for Instance Search}, booktitle = {Content-Based Multimedia Indexing - CBMI}, year = {2018}, month = {09/2018}, publisher = {IEEE}, organization = {IEEE}, address = {La Rochelle, France}, abstract = {

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 of local convolutional features (BLCF) that benefits from saliency weighting to build an efficient image representation. The use of human visual attention models (saliency) allows significant improvements in retrieval performance without the need to conduct region analysis or spatial verification, and without requiring any feature fine tuning. We investigate the impact of different saliency models, finding that higher performance on saliency benchmarks does not necessarily equate to improved performance when used in instance search tasks. The proposed approach outperforms the state-of-the-art on the challenging INSTRE benchmark by a large margin, and provides similar performance on the Oxford and Paris benchmarks compared to more complex methods that use off-the-shelf representations.

Saliency Weighted Convolutional Features for Instance Search from Universitat Polit{\`e}cnica de Catalunya
}, author = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} }