Search Results for author:
Found 33 papers, 8 papers with code
Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning
We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP.
Learning Data Representations with Joint Diffusion Models
Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train.
A Deep Learning Approach for Automatic Detection of Qualitative Features of Lecturing
Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students' works or even providing directions to the teachers on how to enhance the lectures.
EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale
The paper presents a deep neural network-based method for global and local descriptors extraction from a point cloud acquired by a rotating 3D LiDAR.
Large-Scale Topological Radar Localization Using Learned Descriptors
In this work, we propose a method for large-scale topological localization based on radar scan images using learned descriptors.
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
We also identify dominating modality problem when training a multimodal descriptor.
I Want This Product but Different : Multimodal Retrieval with Synthetic Query Expansion
This paper addresses the problem of media retrieval using a multimodal query (a query which combines visual input with additional semantic information in natural language feedback).
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans.
SuperNCN: Neighbourhood consensus network for robust outdoor scenes matching
In this paper, we present a framework for computing dense keypoint correspondences between images under strong scene appearance changes.
Late fusion of deep learning and hand-crafted features for Achilles tendon healing monitoring
In comparison with the previous baseline method, our approach significantly improves correlation in all of the six parameters assessed.
Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks
The obtained estimates show a high correlation with the assessment of expert radiologists, with respect to all key parameters describing healing progress.
Plugin Networks for Inference under Partial Evidence
In this paper, we propose a novel method to incorporate partial evidence in the inference of deep convolutional neural networks.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC 2011 test
Classifying and Visualizing Emotions with Emotional DAN
Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life.
Interest point detectors stability evaluation on ApolloScape dataset
In the recent years, a number of novel, deep-learning based, interest point detectors, such as LIFT, DELF, Superpoint or LF-Net was proposed.
Aggregation of binary feature descriptors for compact scene model representation in large scale structure-from-motion applications
In this paper we present an efficient method for aggregating binary feature descriptors to allow compact representation of 3D scene model in incremental structure-from-motion and SLAM applications.
SConE: Siamese Constellation Embedding Descriptor for Image Matching
Numerous computer vision applications rely on local feature descriptors, such as SIFT, SURF or FREAK, for image matching.
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of image patches (image descriptors).
Estimating Achilles tendon healing progress with convolutional neural networks
Quantitative assessment of a treatment progress in the Achilles tendon healing process - one of the most common musculoskeletal disorder in modern medical practice - is typically a long and complex process: multiple MRI protocols need to be acquired and analysed by radiology experts.
Extracting textual overlays from social media videos using neural networks
The main component of our system, i. e. the text recognition module, is inspired by a convolutional recurrent neural network architecture and we improve its performance using synthetically generated dataset of over 600, 000 images with text prepared by authors specifically for this task.
Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism
Predicting popularity of social media videos before they are published is a challenging task, mainly due to the complexity of content distribution network as well as the number of factors that play part in this process.
Siamese Generative Adversarial Privatizer for Biometric Data
Extensive evaluation on a biometric dataset of fingerprints and cartoon faces confirms usefulness of our simple yet effective method.
I Know How You Feel: Emotion Recognition with Facial Landmarks
Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life.
SocialML: machine learning for social media video creators
In this work, we present a comprehensive overview of machine learning-empowered tools we developed for video creators at Group Nine Media - one of the major social media companies that creates short-form videos with over three billion views per month.
DeepStyle: Multimodal Search Engine for Fashion and Interior Design
In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query.
Random Binary Trees for Approximate Nearest Neighbour Search in Binary Space
Approximate nearest neighbour (ANN) search is one of the most important problems in computer science fields such as data mining or computer vision.
Understanding Aesthetics in Photography using Deep Convolutional Neural Networks
Evaluating aesthetic value of digital photographs is a challenging task, mainly due to numerous factors that need to be taken into account and subjective manner of this process.
Shallow reading with Deep Learning: Predicting popularity of online content using only its title
To take advantage of this phenomenon, we propose a new method based on a bidirectional Long Short-Term Memory (LSTM) neural network designed to predict the popularity of online content using only its title.
Recurrent Neural Networks for Online Video Popularity Prediction
In this paper, we address the problem of popularity prediction of online videos shared in social media.
Evaluation of Hashing Methods Performance on Binary Feature Descriptors
In this paper we evaluate performance of data-dependent hashing methods on binary data.
Deep Alignment Network: A convolutional neural network for robust face alignment
Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches.
Ranked #4 on
Face Alignment
on 300W Split 2
(NME (inter-ocular) metric)
Predicting popularity of online videos using Support Vector Regression
In this work, we propose a regression method to predict the popularity of an online video based on temporal and visual cues.
Boosting Binary Keypoint Descriptors
Binary keypoint descriptors provide an efficient alternative to their floating-point competitors as they enable faster processing while requiring less memory.
Learning Image Descriptors with the Boosting-Trick
The main goal of local feature descriptors is to distinctively represent a salient image region while remaining invariant to viewpoint and illumination changes.
