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] . 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). 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). Exploiting User Interaction and Object Candidates for Instance Retrieval and Object Segmentation. . 2014. (8.97 MB)
(8.97 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). Co-filtering human interaction and object segmentation. . 2015. (1.82 MB)
(1.82 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). Exploring EEG for Object Detection and Retrieval. In ACM International Conference on Multimedia Retrieval (ICMR) . Shanghai, China; 2015. (5.37 MB)
(5.37 MB). Fine-tuning a Convolutional Network for Cultural Event Recognition. . 2015. (11.14 MB)
(11.14 MB). Insight DCU at TRECVID 2015. In TRECVID 2015 Workshop. Gaithersburg, MD, USA: NIST; 2015. (2.13 MB)
(2.13 MB). Layer-wise CNN Surgery for Visual Sentiment Prediction. . 2015. (1.51 MB)
(1.51 MB). NII-HITACHI-UIT at TRECVID 2015 Instance Search. In TRECVID 2015 Workshop. Gaithersburg, MD, USA: NIST; 2015. (1.53 MB)
(1.53 MB). Quality Control in Crowdsourced Object Segmentation. In IEEE International Conference on Image Processing (ICIP), 2015. 2015. (362.33 KB)
(362.33 KB). Rapid Serial Visual Presentation for Relevance Feedback in Image Retrieval with EEG Signals. . 2015. (1.38 MB)
(1.38 MB). Region-oriented Convolutional Networks for Object Retrieval. . 2015. (8.02 MB)
(8.02 MB). Assessment of Crowdsourcing and Gamification Loss in User-Assisted Object Segmentation. Multimedia Tools and Applications. 2016;23(75). (5.05 MB)
(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). Faster R-CNN Features for Instance Search. In CVPR Workshop Deep Vision. 2016. (2.77 MB)
(2.77 MB). Object Tracking in Video with TensorFlow. . 2016. (22.63 MB)
(22.63 MB). Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks. In 1st NIPS Workshop on Large Scale Computer Vision Systems 2016. 2016. (5.66 MB)
(5.66 MB). Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks. . 2016. (27.84 MB)
(27.84 MB). Artificial intelligence suggests recipes based on food photos. Boston: MIT News; 2017.
. Learning Cross-modal Embeddings for Cooking Recipes and Food Images. In CVPR. Honolulu, Hawaii, USA: CVF / IEEE; 2017. (3.37 MB)
(3.37 MB). MIT is building a system that can identify a recipe using pictures of food. Techcrunch; 2017.