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(341.96 KB). Where did I leave my phone ?. Las Vegas, NV, USA: 4th Workshop on Egocentric (First-Person) Vision, CVPR 2016; 2016 . (312.27 KB)
(312.27 KB). Video Saliency Prediction with Deep Neural Networks. . 2019. (2.17 MB)
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(17.14 MB). Time-sensitive Egocentric Image Retrieval for Fidings Objects in Lifelogs. . 2016. (10.23 MB)
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(332.54 KB). The impact of visual saliency prediction in image classification. . 2017. (828.66 KB)
(828.66 KB). Improving Object Segmentation by using EEG signals and Rapid Serial Visual Presentation. Multimedia Tools and Applications. 2015;. (3.86 MB)
(3.86 MB). From Global Image Annotation to Interactive Object Segmentation. Multimedia Tools and Applications. 2014;70. (1.71 MB)
(1.71 MB). Where is my Phone? Personal Object Retrieval from Egocentric Images. In Lifelogging Tools and Applications Workshop in ACM Multimedia. Amsterdam, The Netherlands: ACM; 2016. (1.43 MB)
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(4.42 MB). Temporal Saliency Adaptation in Egocentric Videos. In ECCV 2018 Workshop on Egocentric Perception, Interaction and Computing. Munich, Germany: Extended abstract; 2018. (279.32 KB)
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(1.79 MB). Saliency Weighted Convolutional Features for Instance Search. In Content-Based Multimedia Indexing - CBMI. La Rochelle, France: IEEE; 2018. (3.8 MB)
(3.8 MB). Object segmentation in images using EEG signals. In ACM Multimedia. Orlando, Florida (USA); 2014.
. Insight DCU at TRECVID 2015. In TRECVID 2015 Workshop. Gaithersburg, MD, USA: NIST; 2015. (2.13 MB)
(2.13 MB). Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks. In 2014 TRECVID Workshop. Orlando, Florida (USA): National Institute of Standards and Technology (NIST); 2014. (2.45 MB)
(2.45 MB). Exploring EEG for Object Detection and Retrieval. In ACM International Conference on Multimedia Retrieval (ICMR) . Shanghai, China; 2015. (5.37 MB)
(5.37 MB). Dublin City University and Partners' Participation in the INS and VTT Tracks at TRECVid 2016. In TRECVID Workshop 2016. Gaithersburg, MD, USA; 2016.
. Demonstration of an Open Source Framework for Qualitative Evaluation of CBIR Systems. In ACM Multimedia. Seoul, South Korea: ACM; 2018. (11.05 MB)
(11.05 MB). Bags of Local Convolutional Features for Scalable Instance Search. In ACM International Conference on Multimedia Retrieval (ICMR). New York City, NY; USA: ACM; 2016. (3.73 MB)
(3.73 MB). Object Retrieval with Deep Convolutional Features. In Deep Learning for Image Processing Applications. Amsterdam, The Netherlands: IOS Press; 2017.