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Deep Relative Attributes
TLDR
This work introduces a deep neural network architecture for the task of relative attribute prediction using a convolutional neural network to learn the features by including an additional layer (ranking layer) that learns to rank the images based on these features.
Multiple human 3D pose estimation from multiview images
TLDR
Experimental results indicate that the proposed method achieves substantial improvements over the existing state-of-the-art methods in terms of the probability of correct pose and the mean per joint position error performance measures.
ParsiNLU: A Suite of Language Understanding Challenges for Persian
TLDR
This work introduces ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on, and presents the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compares them with human performance.
Detecting Glaucoma Using 3D Convolutional Neural Network of Raw SD-OCT Optic Nerve Scans
TLDR
A three-dimensional deep learning system using the entire unprocessed OCT optic nerve volumes to distinguish true glaucoma from normals in order to discover any additional imaging biomarkers within the cube through saliency mapping is proposed.
Finding New Diagnostic Information for Detecting Glaucoma using Neural Networks
TLDR
A new approach to automated Glaucoma detection in 3D Spectral Domain Optical Coherence Tomography (OCT) optic nerve scans is described and the Lamina Cribrosa (LC) region can be a valuable source of helpful diagnostic information previously unavailable to doctors during routine clinical care because it lacks a quantitative printout.
Body Field: Structured Mean Field with Human Body Skeleton Model and Shifted Gaussian Edge Potentials
TLDR
The proposed method increases the per-pixel accuracy measure for human body part segmentation and also improves the probability of correct parts metric of human body joint locations.
Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets
TLDR
A three-dimensional (3D) deep learning algorithm to detect glaucoma using spectral-domain optical coherence tomography (SD-OCT) optic nerve head (ONH) cube scans and validate its performance on ethnically diverse real-world datasets and on cropped ONH scans is developed.