Dimitris Samaras

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Associate Professor of Department of Computer Science at Stony Brook University, Director Computer Vision Lab, Computer Science Department at Stony Brook University, Ph.D. in Computer Science, January 2001 at University of Pennsylvania.

Variational Transfer Learning for Fine-grained Few-shot Visual Recognition

Fine-grained few-shot recognition often suffers from the problem of trai...
10/07/2020 ∙ by Jingyi Xu, et al. ∙ 0 ∙ share

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Learning Clusterable Visual Features for Zero-Shot Recognition

In zero-shot learning (ZSL), conditional generators have been widely use...
10/07/2020 ∙ by Jingyi Xu, et al. ∙ 5 ∙ share

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Distribution Matching for Crowd Counting

In crowd counting, each training image contains multiple people, where e...
09/28/2020 ∙ by Boyu Wang, et al. ∙ 4 ∙ share

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Light Direction and Color Estimation from Single Image with Deep Regression

We present a method to estimate the direction and color of the scene lig...
09/18/2020 ∙ by Hassan A. Sial, et al. ∙ 0 ∙ share

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A Study of Human Gaze Behavior During Visual Crowd Counting

In this paper, we describe our study on how humans allocate their attent...
09/14/2020 ∙ by Raji Annadi, et al. ∙ 0 ∙ share

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From Shadow Segmentation to Shadow Removal

The requirement for paired shadow and shadow-free images limits the size...
08/01/2020 ∙ by Hieu Le, et al. ∙ 6 ∙ share

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Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning

Being able to predict human gaze behavior has obvious importance for beh...
05/28/2020 ∙ by Zhibo Yang, et al. ∙ 3 ∙ share

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Towards Better Opioid Antagonists Using Deep Reinforcement Learning

Naloxone, an opioid antagonist, has been widely used to save lives from ...
03/26/2020 ∙ by Jianyuan Deng, et al. ∙ 0 ∙ share

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Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types

The distribution and appearance of nuclei are essential markers for the ...
02/18/2020 ∙ by Le Hou, et al. ∙ 0 ∙ share

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Self-supervised Deformation Modeling for Facial Expression Editing

Recent advances in deep generative models have demonstrated impressive r...
11/02/2019 ∙ by ShahRukh Athar, et al. ∙ 10 ∙ share

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Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated rema...
09/26/2019 ∙ by Robert M. Patton, et al. ∙ 0 ∙ share

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Shadow Removal via Shadow Image Decomposition

We propose a novel deep learning method for shadow removal. Inspired by ...
08/23/2019 ∙ by Hieu Le, et al. ∙ 22 ∙ share

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Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types

Deep learning classifiers for characterization of whole slide tissue mor...
07/09/2019 ∙ by Shahira Abousamra, et al. ∙ 5 ∙ share

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Topology-Preserving Deep Image Segmentation

Segmentation algorithms are prone to make topological errors on fine-sca...
06/12/2019 ∙ by Xiaoling Hu, et al. ∙ 0 ∙ share

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Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer

Quantitative assessment of Tumor-TIL spatial relationships is increasing...
05/26/2019 ∙ by Han Le, et al. ∙ 0 ∙ share

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Weakly Labeling the Antarctic: The Penguin Colony Case

Antarctic penguins are important ecological indicators -- especially in ...
05/08/2019 ∙ by Hieu Le, et al. ∙ 0 ∙ share

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Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion

In this work we introduce Lifting Autoencoders, a generative 3D surface-...
04/26/2019 ∙ by Mihir Sahasrabudhe, et al. ∙ 6 ∙ share

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Label Super Resolution with Inter-Instance Loss

For the task of semantic segmentation, high-resolution (pixel-level) gro...
04/09/2019 ∙ by Maozheng Zhao, et al. ∙ 0 ∙ share

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Latent Space Optimal Transport for Generative Models

Variational Auto-Encoders enforce their learned intermediate latent-spac...
09/16/2018 ∙ by Huidong Liu, et al. ∙ 0 ∙ share

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Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

In this work we introduce Deforming Autoencoders, a generative model for...
06/18/2018 ∙ by Zhixin Shu, et al. ∙ 0 ∙ share

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Unsupervised Histopathology Image Synthesis

Hematoxylin and Eosin stained histopathology image analysis is essential...
12/13/2017 ∙ by Le Hou, et al. ∙ 0 ∙ share

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A+D-Net: Shadow Detection with Adversarial Shadow Attenuation

Single image shadow detection is a very challenging problem because of t...
12/04/2017 ∙ by Hieu Le, et al. ∙ 1 ∙ share

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An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations

Several factors contribute to the appearance of an object in a visual sc...
11/28/2017 ∙ by Mengjiao Wang, et al. ∙ 0 ∙ share

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Improving Heterogeneous Face Recognition with Conditional Adversarial Networks

Heterogeneous face recognition between color image and depth image is a ...
09/08/2017 ∙ by Wuming Zhang, et al. ∙ 0 ∙ share

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Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images

Histopathology images are crucial to the study of complex diseases such ...
04/03/2017 ∙ by Le Hou, et al. ∙ 0 ∙ share

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Geodesic Distance Histogram Feature for Video Segmentation

This paper proposes a geodesic-distance-based feature that encodes globa...
03/31/2017 ∙ by Hieu Le, et al. ∙ 0 ∙ share

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Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images

Classifying the various shapes and attributes of a glioma cell nucleus i...
12/20/2016 ∙ by Veda Murthy, et al. ∙ 0 ∙ share

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Co-localization with Category-Consistent CNN Features and Geodesic Distance Propagation

Co-localization is the problem of localizing objects of the same class u...
12/10/2016 ∙ by Hieu Le, et al. ∙ 0 ∙ share

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Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the p...
11/18/2016 ∙ by Alexandre Abraham, et al. ∙ 0 ∙ share

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Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks

In the context of single-label classification, despite the huge success ...
11/17/2016 ∙ by Le Hou, et al. ∙ 0 ∙ share

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Neural Networks with Smooth Adaptive Activation Functions for Regression

In Neural Networks (NN), Adaptive Activation Functions (AAF) have parame...
08/23/2016 ∙ by Le Hou, et al. ∙ 0 ∙ share

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Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

Convolutional Neural Networks (CNN) are state-of-the-art models for many...
04/29/2015 ∙ by Le Hou, et al. ∙ 0 ∙ share

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Region segmentation for sparse decompositions: better brain parcellations from rest fMRI

Functional Magnetic Resonance Images acquired during resting-state provi...
12/12/2014 ∙ by Alexandre Abraham, et al. ∙ 0 ∙ share

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On the Statistical Efficiency of ℓ_1,p Multi-Task Learning of Gaussian Graphical Models

In this paper, we present ℓ_1,p multi-task structure learning for Gaussi...
07/18/2012 ∙ by Jean Honorio, et al. ∙ 0 ∙ share

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Dimitris Samaras

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