25 citations · 35 across the 3 of their papers we have counts for
6 papers
Improving On-Screen Sound Separation for Open-Domain Videos with Audio-Visual Self-Attention
Efthymios Tzinis, Scott Wisdom, Tal Remez +1
We introduce a state-of-the-art audio-visual on-screen sound separation system which is capable of learning to separate sounds and associate them with on-screen objects by looking…
Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds
Efthymios Tzinis, Scott Wisdom, Aren Jansen +4
Recent progress in deep learning has enabled many advances in sound separation and visual scene understanding. However, extracting sound sources which are apparent in natural video…
Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
Tal Remez, Or Litany, Raja Giryes +1
We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising p…
Learning to Segment via Cut-and-Paste
Tal Remez, Jonathan Huang, Matthew Brown
This paper presents a weakly-supervised approach to object instance segmentation. Starting with known or predicted object bounding boxes, we learn object masks by playing a game of…
Efficient Deformable Shape Correspondence via Kernel Matching
Zorah Lähner, Matthias Vestner, Amit Boyarski +8
We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-…
Deep Class Aware Denoising
Tal Remez, Or Litany, Raja Giryes +1
The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular. At the same…