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What is going on in the world? A display platform for media understanding. In IEEE Multimedia Information Processing and Retrieval (MIPR) Conference. Miami, FL (USA): IEEE; 2018.
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Who watches the watchers? Quality control of the human inspection in production lines using Visual Intensity of Attention. In SAAEI 2018. Barcelona; 2018.
(593.55 KB)
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3D Convolutional Neural Networks for Brain Tumor Segmentation: a comparison of multi-resolution architectures. In Lecture Notes in Computer Vision. Springer; 2017. pp. 150-161.
. 3D hierarchical optimization for multi-view depth map coding. Multimedia Tools and Applications. 2017;.
(4.23 MB)
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3D Medical Image Synthesis using Generative Adversarial Networks. In ACM Europe Celebration of Women in Computing, womENcourage 2017, Barcelona, Spain. 2017.
. 3D Point Cloud Segmentation Using a Fully Connected Conditional Random Field. In The 25th European Signal Processing Conference (EUSIPCO 2017). Kos island, Greece: Eurasip/IEEE; 2017.
(2.34 MB)
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Active Deep Learning for Medical Imaging Segmentation. . 2017.
(2.84 MB)
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Active Deep Learning for Medical Imaging Segmentation. In Medical Image meets NIPS 2017 Workshop. 2017.
(187.43 KB)
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Artificial intelligence suggests recipes based on food photos. Boston: MIT News; 2017.
. Augmented V-Net for infant brain segmentation. In MICCAI Grand Challenge on 6-month Infant Brain MRI Segmentation, MICCAI 2017. 2017.
. Augmented V-Net for White Matter Hyperintensities segmentation. In WMH Segmentation Challenge, Brain-lesion Workshop, MICCAI 2017 . 2017.
. . Class Weighted Convolutional Features for Visual Instance Search. In 28th British Machine Vision Conference (BMVC). London, UK; 2017.
(3.56 MB)
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A closed-loop approach for tracking a humanoid robot using particle filtering and depth data. Intelligent Service Robotics. 2017;10(4):297–312.
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Collaborative voting of 3D features for robust gesture estimation. In International Conference on Acoustics, Speech and Signal Processing. New Orleans, USA; 2017.
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Cost-Effective Active Learning for Melanoma Segmentation. In ML4H: Machine Learning for Health Workshop at NIPS 2017. Long Beach, CA, USA; 2017.
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Detection-aided liver lesion segmentation using deep learning. In ML4H: Machine Learning for Health Workshop at NIPS 2017. 2017.
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Detection-aided medical image segmentation using deep learning. . 2017.
(7.07 MB)
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Disentangling Motion, Foreground and Background Features in Videos. In CVPR 2017 Workshop Brave New Motion Representations. 2017.
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Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster. In International Conference on Computational Science (ICCS). Zurich, Switzerland: Elsevier; 2017.
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Efficient Combination of Pairwise Feature Networks. In: . Neural Connectomics Challenge. Springer International Publishing; 2017.
. . Fine-tuning of CNN models for Instance Search with Pseudo-Relevance Feedback. Long Beach, CA, USA: NIPS 2017 Women in Machine Learning Workshop; 2017.
(341.96 KB)
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From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction. Image and Vision Computing. 2017;.
(1.92 MB)
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Hierarchical Object Detection with Deep Reinforcement Learning. In Deep Learning for Image Processing Applications. Amsterdam, The Netherlands: IOS Press; 2017.
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