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20182025
most citedNormalized Cut Loss for Weakly-supervised CNN Segmentation

53 citations · 120 across the 8 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

stat.ML2018

Disentangled Dynamic Representations from Unordered Data

Leonhard Helminger, Abdelaziz Djelouah, Markus Gross +1

We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. Our approach exploits regularities in sequential data t…

cs.CV2018

Deep Video Color Propagation

Simone Meyer, Victor Cornillère, Abdelaziz Djelouah +2

Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both…

cs.CV2018★ 20 cited

On Regularized Losses for Weakly-supervised CNN Segmentation

Meng Tang, Federico Perazzi, Abdelaziz Djelouah +3

Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segment…

cs.CV2018★ 53 cited

Normalized Cut Loss for Weakly-supervised CNN Segmentation

Meng Tang, Abdelaziz Djelouah, Federico Perazzi +2

Most recent semantic segmentation methods train deep convolutional neural networks with fully annotated masks requiring pixel-accuracy for good quality training. Common weakly-supe…

cs.CV2018

PhaseNet for Video Frame Interpolation

Simone Meyer, Abdelaziz Djelouah, Brian McWilliams +3

Most approaches for video frame interpolation require accurate dense correspondences to synthesize an in-between frame. Therefore, they do not perform well in challenging scenarios…