@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.} } @article {aAssens, title = {Scanpath and Saliency Prediction on 360 Degree Images}, journal = {Elsevier Signal Processing: Image Communication}, year = {2018}, abstract = {

We introduce deep neural networks for scanpath and saliency prediction trained on 360-degree images. The scanpath prediction model called SaltiNet is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation using a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. We also show how a similar architecture achieves state-of-the-art performance for the related task of saliency map prediction. Our source code and trained models available here.

}, url = {https://www.sciencedirect.com/science/article/pii/S0923596518306209}, author = {Assens, Marc and McGuinness, Kevin and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @conference {cPana, title = {SalGAN: Visual Saliency Prediction with Generative Adversarial Networks}, booktitle = {CVPR 2017 Scene Understanding Workshop (SUNw)}, year = {2017}, address = {Honolulu, Hawaii, USA}, abstract = {

We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples. The first stage of the network consists of a generator model whose weights are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency maps. The resulting prediction is processed by a discriminator network trained to solve a binary classification task between the saliency maps generated by the generative stage and the ground truth ones. Our experiments show how adversarial training allows reaching state-of-the-art performance across different metrics when combined with a widely-used loss function like BCE.

}, url = {https://arxiv.org/abs/1701.01081}, author = {Pan, Junting and Cristian Canton-Ferrer and McGuinness, Kevin and O{\textquoteright}Connor, N. and Jordi Torres and Elisa Sayrol and Xavier Gir{\'o}-i-Nieto} } @conference {cAssens, title = {SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes}, booktitle = {ICCV Workshop on Egocentric Perception, Interaction and Computing}, year = {2017}, month = {07/2017}, publisher = {IEEE}, organization = {IEEE}, address = {Venice, Italy}, abstract = {

We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks.

Winner of three awards at the Salient 360 Challenge at IEEE ICME 2017 (Hong Kong): Best Scan Path, Best Student Scan-path and Audience Award.

SaltiNet: The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, isbn = {978-1-5386-1034-3}, doi = {10.1109/ICCVW.2017.275}, url = {http://ieeexplore.ieee.org/document/8265485/}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cPan, title = {Shallow and Deep Convolutional Networks for Saliency Prediction}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition, CVPR}, year = {2016}, month = {06/2016}, publisher = {Computer Vision Foundation / IEEE}, organization = {Computer Vision Foundation / IEEE}, address = {Las Vegas, NV, USA}, abstract = {

The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train end-to-end architectures that are both fast and accurate. Two designs are proposed: a shallow convnet trained from scratch, and a another deeper solution whose first three layers are adapted from another network trained for classification. To the authors knowledge, these are the first end-to-end CNNs trained and tested for the purpose of saliency prediction.

}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pan_Shallow_and_Deep_CVPR_2016_paper.pdf}, author = {Pan, Junting and McGuinness, Kevin and Elisa Sayrol and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} }