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Found 8 papers, 2 papers with code
MOVE: Unsupervised Movable Object Segmentation and Detection
We introduce MOVE, a novel method to segment objects without any form of supervision.
Ranked #2 on
Unsupervised Saliency Detection
on DUTS
Generative Adversarial Learning via Kernel Density Discrimination
We show a boost in the quality of generated samples with respect to FID from 10% to 40% compared to the baseline.
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity
Moreover, we show that the Kalman Filter dynamical model for the evolution of the unknown parameters can be used to capture the gradient dynamics of advanced methods such as Momentum and Adam.
Emergence of Object Segmentation in Perturbed Generative Models
To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background.
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.
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.
