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The aim of this work is to develop and study the technology of automatic determination of indications for 2RT-laser treatment of AMD by SD-OCT images based on artificial intelligence methods. This is necessary to improve the accuracy and ...

This paper proposes an approach for detecting smoke in industrial production using computer vision. The task of detecting smoke and fire can be framed as a detection problem, making modern convolutional neural network models well-suited for this ...

In existing deep learning image steganography methods, the neural network structure contains ineffective or complicated standard convolutional layers. These convolutional layers do not significantly improve performance, but instead increase ...

Existing works focus on using convolutional networks as the feature map extractors for both scene and head in gaze target detection tasks. Cordonnier [1] demonstrated that attention mechanisms can entirely substitute convolutional layers and ...

Accurate traffic forecasting is crucial for the development of Intelligent Transportation Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional forecasting methods, however, struggle with the irregular traffic time series ...

This study embarked on a comprehensive exploration of deploying lightweight CNN models for the real-time classification of bird species, particularly focusing on their application within edge computing environments. Given the critical importance ...

Deep graph clustering, which reveals the intrinsic structure and underlying relationship of graph node data, has become a highly concerning and challenging research task for graph-structured data. In recent years, existing graph clustering methods ...$\mathbf{}$$\mathbf{}$$\mathbf{}$$\mathbf{}$$\mathbf{}$$\mathbf{}$

The transfer learning method enables the use of a pretrained convolutional network to efficiently model a secondary domain with less data. In this article 18 public convolutional networks of different architecture and depth, pretrained on ImageNet,...

Point cloud upsampling can improve the resolutions of point clouds and maintain the forms of point clouds, which has attracted more and more attention in recent years. However, upsampling networks sometimes generate point clouds with unclear contours and ...

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input ...

Event cameras are a kind of radically novel vision sensors. Unlike traditional standard cameras which acquire full images at a fixed rate, event cameras capture brightness changes for each pixel asynchronously. As a result, the output of event camera is ...

This paper introduces a new artificial intelligence software which is capable of controlling devices using fingers in the air. With Ubimouse, touch-panels, restaurant ordering systems, ATM systems, and etc., which are commonly used by various people in ...

The crux of homography estimation is that the homography is characterized by the geometric correspondences between two related images rather than appearance features, which differs from typical image recognition tasks. Existing methods either decompose ...

The wavelet scattering transform is an invariant and stable signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the ...

Image classification models take image pixels as input and predict labels in a predefined taxonomy. While contextual information (e.g. text surrounding an image) can provide valuable orthogonal signals to improve classification, the typical setting in ...

We present a new framework for interior scene synthesis that combines a high-level relation graph representation with spatial prior neural networks. We observe that prior work on scene synthesis is divided into two camps: object-oriented approaches (...

Today deep learning is taking its rise in hydrometeorological applications, and it is critical to extensively evaluate its prediction performance and robustness. In our study, we use deep all convolutional neural networks for radar-based ...

A deep learning approach for analyzing DNase-seq datasets is presented, which has promising potentials for unraveling biological underpinnings on transcription regulation mechanisms. Further understanding of these mechanisms can lead to important ...

We present a convolutional neural network based approach for indoor scene synthesis. By representing 3D scenes with a semantically-enriched image-based representation based on orthographic top-down views, we learn convolutional object placement priors ...

Over the last decade, the number of devices per person has increased substantially. This poses a challenge for cookie-based personalization applications, such as online search and advertising, as it narrows the personalization signal to a single device ...