activity
20162026
most citedDeep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection

94 citations · 249 across the 12 of their papers we have counts for

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12 papers · 1 filter

cs.CV2023

CycleCL: Self-supervised Learning for Periodic Videos

Matteo Destro, Michael Gygli

Analyzing periodic video sequences is a key topic in applications such as automatic production systems, remote sensing, medical applications, or physical training. An example is co…

cs.CV2021

Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types

Thomas Mensink, Jasper Uijlings, Alina Kuznetsova +2

Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art comp…

cs.CV2019

Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections

Theodora Kontogianni, Michael Gygli, Jasper Uijlings +1

In interactive object segmentation a user collaborates with a computer vision model to segment an object. Recent works employ convolutional neural networks for this task: Given an…

cs.CV2019★ 2 cited

Natural Vocabulary Emerges from Free-Form Annotations

Jordi Pont-Tuset, Michael Gygli, Vittorio Ferrari

We propose an approach for annotating object classes using free-form text written by undirected and untrained annotators. Free-form labeling is natural for annotators, they intuiti…

cs.CV2019

Efficient Object Annotation via Speaking and Pointing

Michael Gygli, Vittorio Ferrari

Deep neural networks deliver state-of-the-art visual recognition, but they rely on large datasets, which are time-consuming to annotate. These datasets are typically annotated in t…

cs.CV2018

Fast Object Class Labelling via Speech

Michael Gygli, Vittorio Ferrari

Object class labelling is the task of annotating images with labels on the presence or absence of objects from a given class vocabulary. Simply asking one yes/no question per class…

cs.CV2018★ 34 cited

PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation

Ana García del Molino, Michael Gygli

Highlight detection models are typically trained to identify cues that make visual content appealing or interesting for the general public, with the objective of reducing a video t…

cs.CV2017★ 34 cited

Interactive Video Object Segmentation in the Wild

Arnaud Benard, Michael Gygli

In this paper we present our system for human-in-the-loop video object segmentation. The backbone of our system is a method for one-shot video object segmentation. While fast, this…

cs.CV2017★ 1 cited

Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks

Michael Gygli

Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition, video indexing, summarization and editing. Previous work typically…

cs.CV2017

Query-adaptive Video Summarization via Quality-aware Relevance Estimation

Arun Balajee Vasudevan, Michael Gygli, Anna Volokitin +1

Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a…

cs.CV2017

PathTrack: Fast Trajectory Annotation with Path Supervision

Santiago Manen, Michael Gygli, Dengxin Dai +1

Progress in Multiple Object Tracking (MOT) has been historically limited by the size of the available datasets. We present an efficient framework to annotate trajectories and use i…

cs.CV2016★ 38 cited

Video2GIF: Automatic Generation of Animated GIFs from Video

Michael Gygli, Yale Song, Liangliang Cao

We introduce the novel problem of automatically generating animated GIFs from video. GIFs are short looping video with no sound, and a perfect combination between image and video t…