94 citations · 249 across the 12 of their papers we have counts for
17 papers
VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions
Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh +10
The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds. This transition introduces an ``extreme cold-start'' problem: unli…
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…
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…
Towards Reusable Network Components by Learning Compatible Representations
Michael Gygli, Jasper Uijlings, Vittorio Ferrari
This paper proposes to make a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the tra…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
Michael Gygli, Mohammad Norouzi, Anelia Angelova
We approach structured output prediction by optimizing a deep value network (DVN) to precisely estimate the task loss on different output configurations for a given input. Once the…
AENet: Learning Deep Audio Features for Video Analysis
Naoya Takahashi, Michael Gygli, Luc Van Gool
We propose a new deep network for audio event recognition, called AENet. In contrast to speech, sounds coming from audio events may be produced by a wide variety of sources. Furthe…
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…
Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection
Naoya Takahashi, Michael Gygli, Beat Pfister +1
We propose a novel method for Acoustic Event Detection (AED). In contrast to speech, sounds coming from acoustic events may be produced by a wide variety of sources. Furthermore, d…