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. Assessment of Crowdsourcing and Gamification Loss in User-Assisted Object Segmentation. Multimedia Tools and Applications. 2016;23(75). 
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(5.05 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). Budget-aware Semi-Supervised Semantic and Instance Segmentation. In CVPR 2019 DeepVision Workshop. Long Beach, CA, USA: OpenCVF; 2019. (6.59 MB)
(6.59 MB). Click’n’Cut: Crowdsourced Interactive Segmentation with Object Candidates. In 3rd International ACM Workshop on Crowdsourcing for Multimedia (CrowdMM). Orlando, Florida (USA); 2014. (1017.73 KB)
(1017.73 KB). Co-filtering human interaction and object segmentation. . 2015. (1.82 MB)
(1.82 MB). Computer Vision beyond the visible: Image understanding through language. . Signal Theory and Communications. [Barcelona]: Universitat Politecnica de Catalunya; 2019.
. Cross-modal Embeddings for Video and Audio Retrieval. In ECCV 2018 Women in Computer Vision Workshop. Munich, Germany: Springer; 2018. (1.07 MB)
(1.07 MB). Crowdsourced Object Segmentation with a Game. . 2013. (1.34 MB)
(1.34 MB). Crowdsourced Object Segmentation with a Game. In ACM Workshop on Crowdsourcing for Multimedia (CrowdMM). Barcelona; 2013. (1.22 MB)
(1.22 MB). Cultural Event Recognition with Visual ConvNets and Temporal Models. In CVPR ChaLearn Looking at People Workshop 2015. 2015. (1.09 MB)
(1.09 MB). Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction. In 1st International Workshop on Affect and Sentiment in Multimedia. Brisbane, Australia: ACM; 2015. (506.22 KB)
(506.22 KB). Exploiting User Interaction and Object Candidates for Instance Retrieval and Object Segmentation. . 2014. (8.97 MB)
(8.97 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). Faster R-CNN Features for Instance Search. In CVPR Workshop Deep Vision. 2016. (2.77 MB)
(2.77 MB). Fine-tuning a Convolutional Network for Cultural Event Recognition. . 2015. (11.14 MB)
(11.14 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). Insight DCU at TRECVID 2015. In TRECVID 2015 Workshop. Gaithersburg, MD, USA: NIST; 2015. (2.13 MB)
(2.13 MB). Inverse Cooking: Recipe Generation from Food Images. In CVPR. Long Beach, CA, USA: OpenCVF / IEEE; 2019.
. Layer-wise CNN Surgery for Visual Sentiment Prediction. . 2015. (1.51 MB)
(1.51 MB). Learning Cross-modal Embeddings for Cooking Recipes and Food Images. In CVPR. Honolulu, Hawaii, USA: CVF / IEEE; 2017. (3.37 MB)
(3.37 MB). Mask-guided sample selection for Semi-Supervised Instance Segmentation. Multimedia Tools and Applications. 2020;. (2.2 MB)
(2.2 MB). MIT is building a system that can identify a recipe using pictures of food. Techcrunch; 2017.
. NII-HITACHI-UIT at TRECVID 2015 Instance Search. In TRECVID 2015 Workshop. Gaithersburg, MD, USA: NIST; 2015. (1.53 MB)
(1.53 MB). Object Retrieval with Deep Convolutional Features. In Deep Learning for Image Processing Applications. Amsterdam, The Netherlands: IOS Press; 2017.