15 citations · 23 across the 7 of their papers we have counts for
6 papers · 1 filter
MOVE: Unsupervised Movable Object Segmentation and Detection
Adam Bielski, Paolo Favaro
We introduce MOVE, a novel method to segment objects without any form of supervision. MOVE exploits the fact that foreground objects can be shifted locally relative to their initia…
Emergence of Object Segmentation in Perturbed Generative Models
Adam Bielski, Paolo Favaro
We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation…
I Know How You Feel: Emotion Recognition with Facial Landmarks
Ivona Tautkute, Tomasz Trzcinski, Adam Bielski
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 i…
Extracting textual overlays from social media videos using neural networks
Adam Słucki, Tomasz Trzcinski, Adam Bielski +1
Textual overlays are often used in social media videos as people who watch them without the sound would otherwise miss essential information conveyed in the audio stream. This is w…
Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism
Adam Bielski, Tomasz Trzcinski
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…
SocialML: machine learning for social media video creators
Tomasz Trzcinski, Adam Bielski, Paweł Cyrta +1
In the recent years, social media have become one of the main places where creative content is being published and consumed by billions of users. Contrary to traditional media, soc…