8 citations · 13 across the 6 of their papers we have counts for
10 papers
Reviving Iterative Training with Mask Guidance for Interactive Segmentation
Konstantin Sofiiuk, Ilia A. Petrov, Anton Konushin
Recent works on click-based interactive segmentation have demonstrated state-of-the-art results by using various inference-time optimization schemes. These methods are considerably…
Road images augmentation with synthetic traffic signs using neural networks
Anton Konushin, Boris Faizov, Vlad Shakhuro
Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in…
Towards General Purpose Geometry-Preserving Single-View Depth Estimation
Mikhail Romanov, Nikolay Patatkin, Anna Vorontsova +3
Single-view depth estimation (SVDE) plays a crucial role in scene understanding for AR applications, 3D modeling, and robotics, providing the geometry of a scene based on a single…
Learning High-Resolution Domain-Specific Representations with a GAN Generator
Danil Galeev, Konstantin Sofiiuk, Danila Rukhovich +3
In recent years generative models of visual data have made a great progress, and now they are able to produce images of high quality and diversity. In this work we study representa…
Foreground-aware Semantic Representations for Image Harmonization
Konstantin Sofiiuk, Polina Popenova, Anton Konushin
Image harmonization is an important step in photo editing to achieve visual consistency in composite images by adjusting the appearances of foreground to make it compatible with ba…
IterDet: Iterative Scheme for Object Detection in Crowded Environments
Danila Rukhovich, Konstantin Sofiiuk, Danil Galeev +2
Deep learning-based detectors usually produce a redundant set of object bounding boxes including many duplicate detections of the same object. These boxes are then filtered using n…
f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation
Konstantin Sofiiuk, Ilia Petrov, Olga Barinova +1
Deep neural networks have become a mainstream approach to interactive segmentation. As we show in our experiments, while for some images a trained network provides accurate segment…
Training Deep SLAM on Single Frames
Igor Slinko, Anna Vorontsova, Dmitry Zhukov +2
Learning-based visual odometry and SLAM methods demonstrate a steady improvement over past years. However, collecting ground truth poses to train these methods is difficult and exp…
DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation
Pavel Kirsanov, Airat Gaskarov, Filipp Konokhov +7
We present a novel dataset for training and benchmarking semantic SLAM methods. The dataset consists of 200 long sequences, each one containing 3000-5000 data frames. We generate t…
AdaptIS: Adaptive Instance Selection Network
Konstantin Sofiiuk, Olga Barinova, Anton Konushin
We present Adaptive Instance Selection network architecture for class-agnostic instance segmentation. Given an input image and a point , it generates a mask for the object…