Results 31 to 40 of about 173,502 (284)
, 2017 In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data.
Brox, Thomas +5 more
core +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with
Kaiming He +3 more
openaire +4 more sources
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction [PDF]
, 2015 Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established
Jiang Y.-G. +6 more
core +2 more sources
Remote Sensing, 2017 Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to
Jie Wang +4 more
doaj +1 more source
Teaching Compositionality to CNNs
, 2017 Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions.
George, Dileep +5 more
core +1 more source
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
, 2015 This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs).
Derpanis, Konstantinos G. +2 more
core +1 more source
Journal of Marine Science and Engineering, 2021 To meet the increasing sailing demand of the Northeast Passage of the Arctic, a daily prediction model of sea ice concentration (SIC) based on the convolutional long short-term memory network (ConvLSTM) algorithm was proposed in this study.
Quanhong Liu +4 more
doaj +1 more source
Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks
, 2021 Convolutional neural networks (CNNs) have been shown to achieve optimal approximation and estimation error rates (in minimax sense) in several function classes.
Oono, Kenta, Suzuki, Taiji
core
FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah +7 more
wiley +1 more source