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Found 11 papers, 6 papers with code
Deep Learning for Rheumatoid Arthritis: Joint Detection and Damage Scoring in X-rays
Recent advancements in computer vision promise to automate medical image analysis.
Training DNNs in O(1) memory with MEM-DFA using Random Matrices
The algorithm is based on the more biologically plausible alternatives of the backpropagation (BP): direct feedback alignment (DFA) and feedback alignment (FA), which use random matrices to propagate error.
Augmentation Inside the Network
We propose a modification that is 30% faster than the flip test-time augmentation and achieves the same results for CIFAR-100.
Holistic Multi-View Building Analysis in the Wild with Projection Pooling
To this end, we introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings.
Large-scale mammography CAD with Deformable Conv-Nets
State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules.
Augmentation for small object detection
We evaluate different pasting augmentation strategies, and ultimately, we achieve 9. 7\% relative improvement on the instance segmentation and 7. 1\% on the object detection of small objects, compared to the current state of the art method on
The Devil is in the Decoder: Classification, Regression and GANs
Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.
Attention-based Extraction of Structured Information from Street View Imagery
We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84. 2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72. 46%.
Ranked #1 on
Optical Character Recognition (OCR)
on FSNS - Test
Semantic Instance Segmentation via Deep Metric Learning
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.
Speed/accuracy trade-offs for modern convolutional object detectors
On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.
Ranked #209 on
Object Detection
on COCO test-dev
(using extra training data)
Rethinking the Inception Architecture for Computer Vision
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.
Ranked #8 on
Retinal OCT Disease Classification
on OCT2017
